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Title: GitHub - axruff/DeepLearning: A collection of research papers, datasets and software on Deep Learning

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💎 Neural Networkshttps://patch-diff.githubusercontent.com/axruff/DeepLearning#neural-networks
⭕ Modelshttps://patch-diff.githubusercontent.com/axruff/DeepLearning#models
Multi-levelhttps://patch-diff.githubusercontent.com/axruff/DeepLearning#multi-level
Context and Attentionhttps://patch-diff.githubusercontent.com/axruff/DeepLearning#context-and-attention
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Optimization and Regularisationhttps://patch-diff.githubusercontent.com/axruff/DeepLearning#optimization-and-regularisation
Pruning, Compressionhttps://patch-diff.githubusercontent.com/axruff/DeepLearning#pruning-and-compression
📊 Analysis and Interpretabilityhttps://patch-diff.githubusercontent.com/axruff/DeepLearning#analysis-and-interpretability
📜 Taskshttps://patch-diff.githubusercontent.com/axruff/DeepLearning#tasks
Segmentationhttps://patch-diff.githubusercontent.com/axruff/DeepLearning#segmentation
Instance Segmentationhttps://patch-diff.githubusercontent.com/axruff/DeepLearning#instance-segmentation
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TL - Transfer Learninghttps://patch-diff.githubusercontent.com/axruff/DeepLearning#transfer-learning
GM - Generative Modellinghttps://patch-diff.githubusercontent.com/axruff/DeepLearning#generative-modelling
WS - Weakly Supervised Learninghttps://patch-diff.githubusercontent.com/axruff/DeepLearning#weakly-supervised
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IRL - Inverse Reinforcement Learninghttps://patch-diff.githubusercontent.com/axruff/DeepLearning#inverse-reinforcement-learning
🎁 Datasetshttps://patch-diff.githubusercontent.com/axruff/DeepLearning#datasets
⚔ Benchmarkshttps://patch-diff.githubusercontent.com/axruff/DeepLearning#benchmarks
🌍 Applicationshttps://patch-diff.githubusercontent.com/axruff/DeepLearning#applications
Applications: Medical Imaginghttps://patch-diff.githubusercontent.com/axruff/DeepLearning#applications-medical-imaging
Applications: X-ray Imaginghttps://patch-diff.githubusercontent.com/axruff/DeepLearning#applications-x-ray-imaging
Applications: Image Registrationhttps://patch-diff.githubusercontent.com/axruff/DeepLearning#applications-image-registration
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1998 - [LeNet]: Gradient-based learning applied to document recognitionhttps://ieeexplore.ieee.org/document/726791
2012 - [AlexNet] ImageNet Classification with Deep Convolutional Neural Networkshttps://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
2013 - Learning Hierarchical Features for Scene Labelinghttp://yann.lecun.com/exdb/publis/pdf/farabet-pami-13.pdf
2013 - [R-CNN] Rich feature hierarchies for accurate object detection and semantic segmentationhttps://arxiv.org/abs/1311.2524
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2014 - [OverFeat]: Integrated Recognition, Localization and Detection using Convolutional Networkshttps://arxiv.org/pdf/1312.6229.pdf
2014 - [Seq2Seq]: Sequence to Sequence Learning with Neural Networkshttps://arxiv.org/pdf/1409.3215.pdf
2014 - [VGG] Very Deep Convolutional Networks for Large-Scale Image Recognitionhttps://arxiv.org/abs/1409.1556
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2014 - [GoogleNet] Going Deeper with Convolutionshttps://arxiv.org/abs/1409.4842
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2014 - Neural Turing Machineshttps://arxiv.org/abs/1410.5401
2015 - [ResNet] Deep Residual Learning for Image Recognitionhttps://arxiv.org/abs/1512.03385
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2015 - Spatial Transformer Networkshttps://arxiv.org/abs/1506.02025
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2016 - [WRN]: Wide Residual Networkshttps://arxiv.org/abs/1605.07146
[github]https://github.com/szagoruyko/wide-residual-networks
2015 - [FCN] Fully Convolutional Networks for Semantic Segmentationhttps://arxiv.org/abs/1411.4038
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2015 - [U-net]: Convolutional networks for biomedical image segmentationhttps://arxiv.org/abs/1505.04597
2016 - [Xception]: Deep Learning with Depthwise Separable Convolutionshttps://arxiv.org/abs/1610.02357
Implementationhttps://colab.research.google.com/drive/1BT_t64JCzr8ge51orG8uLBLIL7w1Hos4
2016 - [V-Net]: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationhttps://arxiv.org/abs/1606.04797
2017 - [MobileNets]: Efficient Convolutional Neural Networks for Mobile Vision Applicationshttps://arxiv.org/abs/1704.04861
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentialshttps://arxiv.org/abs/1210.5644
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2018 - [TernausNet]: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentationhttps://arxiv.org/abs/1801.05746
2018 - CubeNet: Equivariance to 3D Rotation and Translationhttp://openaccess.thecvf.com/content_ECCV_2018/papers/Daniel_Worrall_CubeNet_Equivariance_to_ECCV_2018_paper.pdf
[github]https://github.com/deworrall92/cubenet
[video]https://www.youtube.com/watch?v=TlzRyHbWeP0&feature=youtu.be
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2018 - Deep Rotation Equivariant Networkhttps://arxiv.org/abs/1705.08623
[github]https://github.com/ZJULearning/DREN/raw/master/img/rotate_equivariant.png
https://github.com/ZJULearning/DREN/raw/master/img/rotate_equivariant.png
2018 - ArcFace: Additive Angular Margin Loss for Deep Face Recognitionhttps://arxiv.org/abs/1801.07698
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2019 - [PacNet]: Pixel-Adaptive Convolutional Neural Networkshttps://arxiv.org/abs/1904.05373
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2019 - Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregationhttps://arxiv.org/abs/1903.02120v3
[github]https://github.com/LinZhuoChen/DUpsampling
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2019 - Panoptic Feature Pyramid Networkshttp://openaccess.thecvf.com/content_CVPR_2019/html/Kirillov_Panoptic_Feature_Pyramid_Networks_CVPR_2019_paper.html
2019 - [DeeperLab]: Single-Shot Image Parserhttps://arxiv.org/abs/1902.05093
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2019 - [EfficientNet]: Rethinking Model Scaling for Convolutional Neural Networkshttps://arxiv.org/abs/1905.11946
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2019 - Hamiltonian Neural Networkshttps://arxiv.org/abs/1906.01563
https://camo.githubusercontent.com/42339f02d68e2b5d45b22e56c15718d20a10eed1eeb4f59f18c899c9b6ef2326/68747470733a2f2f6772657964616e75732e6769746875622e696f2f6173736574732f68616d696c746f6e69616e2d6e6e732f6f766572616c6c2d696465612e706e67
2020 - Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysishttps://arxiv.org/abs/2002.08725
https://camo.githubusercontent.com/cb67c6614e2bd7594b0c2caee5680d74e09fb4a626f242d5f63d4a64bbcf71d3/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f67726f756e6461692d7765622d70726f642f6d656469612f75736572732f757365725f31342f70726f6a6563745f3430383933322f696d616765732f78312e706e67
2020 - Neural Operator: Graph Kernel Network for Partial Differential Equationshttps://arxiv.org/abs/2003.03485
https://camo.githubusercontent.com/f0d2057df2fb6bf7e78a5a9eaf7a28e041fb160fe0c283203ccb8f928584d24f/68747470733a2f2f692e70696e696d672e636f6d2f353634782f63612f64332f34612f63616433346133653665663834343531353233396430626138306434306638612e6a7067
2021 - Learning Neural Network Subspaceshttps://arxiv.org/abs/2102.10472
https://camo.githubusercontent.com/e2e4c582f84ecb26810627c79ce18f85a59f595fa8228e7673c70890b4e98f1d/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f62393564656662372d336332332d343939372d396538332d3938323035636463376233382f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313033303125324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130333031543134353231355a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d3763336563623430336535366634373239323935376137663032396663376535333866363863386432346633613866353066623138663232353661633665653526582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
https://patch-diff.githubusercontent.com/axruff/DeepLearning#multi-level
2014 - [SPP-Net] Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitionhttps://arxiv.org/abs/1406.4729
https://camo.githubusercontent.com/bf9f600af351c0e2fa113d6df04ff29cdc7e4556978f799409c87b030f7854fb/687474703a2f2f6b61696d696e6768652e636f6d2f6563637631347370706e65742f696d672f7370706e65742e6a7067
2016 - [ParseNet]: Looking Wider to See Betterhttps://arxiv.org/abs/1506.04579
https://camo.githubusercontent.com/8669df2baa0288c68fcb338a7dbe9a47bca11e2ea09e6e8079ea02b1018bef1b/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f3730302f312a645268476574484172495f6273364964694946686b412e706e67
2016 - [PSPNet]: Pyramid Scene Parsing Networkhttps://arxiv.org/abs/1612.01105v2
[github]https://github.com/hszhao/PSPNet
https://camo.githubusercontent.com/783ee50c5c9988317d610a1efa523dcab6460c47d7826510b442613352b1fb4d/68747470733a2f2f68737a68616f2e6769746875622e696f2f70726f6a656374732f7073706e65742f666967757265732f7073706e65742e706e67
2016 - [DeepLab]: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFshttps://arxiv.org/pdf/1606.00915.pdf
2015 - Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Nethttps://arxiv.org/abs/1511.06881
https://camo.githubusercontent.com/60a6100400bb71ad1f91f6ad5acac8cd309219286103e3eb7bc5921dba634110/68747470733a2f2f6d656469612e737072696e6765726e61747572652e636f6d2f6f726967696e616c2f737072696e6765722d7374617469632f696d6167652f63687025334131302e313030372532463937382d332d3331392d34363435342d315f33392f4d656469614f626a656374732f3431393937385f315f456e5f33395f466967315f48544d4c2e676966
2016 - Attention to Scale: Scale-aware Semantic Image Segmentationhttps://arxiv.org/abs/1511.03339
https://camo.githubusercontent.com/6268a64aa8cfb395c03f96b533ee3d3f8e08e175f80c3052c1a1f662a47eaa6e/687474703a2f2f6c69616e6763686965686368656e2e636f6d2f6669672f617474656e74696f6e2e6a7067
2017 - Rethinking Atrous Convolution for Semantic Image Segmentationhttps://arxiv.org/pdf/1706.05587.pdf
2017 - Feature Pyramid Networks for Object Detectionhttps://arxiv.org/abs/1612.03144
https://camo.githubusercontent.com/1ee08a42c1f898618a380a050b11d65d4a64912fb44d13b26e3461964cdf8ee2/68747470733a2f2f312e62702e626c6f6773706f742e636f6d2f2d51302d6f5f656a384244552f5754596e533536386e50492f41414141414141414144512f545442637a7250495169384976585a726a793373755244426c6f5f7031704f4e51434c63422f733634302f72312e706e67
2018 - [DeepLabv3]: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationhttps://arxiv.org/abs/1802.02611
https://camo.githubusercontent.com/1d77f77c8e1ab486966f109cf95dad1656aa1a00d63190792a003acf93fece62/68747470733a2f2f322e62702e626c6f6773706f742e636f6d2f2d67786e625a39773244726f2f57714d4f51544a5f7a7a492f41414141414141414365412f64794c676b5935546e464566326a366a795844584944576a5f7772624868746551434c63424741732f733634302f696d616765322e706e67
2019 - [FastFCN]: Rethinking Dilated Convolution in the Backbone for Semantic Segmentationhttps://arxiv.org/abs/1903.11816v1
[github]https://github.com/wuhuikai/FastFCN
https://camo.githubusercontent.com/0f641fa45c749e5d705fdf48853faf56c189206c64ce698ca009f399bfb0dd0f/687474703a2f2f77756875696b61692e6d652f4661737446434e50726f6a6563742f696d616765732f4672616d65776f726b2e706e67
2019 - Making Convolutional Networks Shift-Invariant Againhttps://arxiv.org/abs/1904.11486
https://camo.githubusercontent.com/0265d2a637330c50f816ea44312b9c9856552cbe4938b0d178ca86280f2836f8/68747470733a2f2f692e70696e696d672e636f6d2f353634782f37322f61322f35632f37326132356337643837653163346466656634356265633831616465653265372e6a7067
2019 - [LEDNet]: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentationhttps://arxiv.org/abs/1905.02423v1
https://camo.githubusercontent.com/8f2b450af8dbb9601d0dae42d552ae18db8cb1381a83835d314cab1a05cb8aae/687474703a2f2f7777772e70726f6772616d6d6572736f756768742e636f6d2f696d616765732f3338372f65623565383331353934343231303664313966626437393639386532393965622e706e67
2019 - Feature Pyramid Encoding Network for Real-time Semantic Segmentationhttps://arxiv.org/abs/1909.08599v1
https://camo.githubusercontent.com/c8782af74a6ab6b02945e2c601c7411baeff32517af80f25bb30780daccc3b3e/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f67726f756e6461692d7765622d70726f642f6d656469612532467573657273253246757365725f32393036353425324670726f6a6563745f333930363933253246696d616765732532464650454e65742e706e67
2019 - Efficient Segmentation: Learning Downsampling Near Semantic Boundarieshttps://arxiv.org/abs/1907.07156
https://camo.githubusercontent.com/2593541be2d54ff80b70fb1c04fae90a76f34422a97de5ca969fcd089164acd0/68747470733a2f2f696d616765732e6465657061692e6f72672f636f6e7665727465642d7061706572732f313930372e30373135362f78352e706e67
2019 - PointRend: Image Segmentation as Renderinghttps://arxiv.org/abs/1912.08193
https://camo.githubusercontent.com/428087f85f12f324bba2416f36713d37ee018bca0e10039c8a65a8f74b120f5b/68747470733a2f2f6d656469612e61727869762d76616e6974792e636f6d2f72656e6465722d6f75747075742f313937363730312f78332e706e67
2019 - Fixing the train-test resolution discrepancyhttps://arxiv.org/abs/1906.06423
https://raw.githubusercontent.com/facebookresearch/FixRes/master/image/image2.png
https://patch-diff.githubusercontent.com/axruff/DeepLearning#context-and-attention
2016 - Image Captioning with Semantic Attentionhttps://arxiv.org/abs/1603.03925
https://camo.githubusercontent.com/dc28dd5b197dabbfa479709d263ce24427f058116514df7c32ba26094056191f/687474703a2f2f63646e2d616b2e662e73742d686174656e612e636f6d2f696d616765732f666f746f6c6966652f502f504446616e67656c746f70312f32303136303430362f32303136303430363136313033352e706e67
2018 - [EncNet] Context Encoding for Semantic Segmentationhttps://arxiv.org/abs/1803.08904v1
[github]https://github.com/zhanghang1989/PyTorch-Encoding
https://camo.githubusercontent.com/4c45ee2b606633c9c4d04a75218afa71508e6f9c7cc25a1810f41b00068b818d/68747470733a2f2f656e637279707465642d74626e302e677374617469632e636f6d2f696d616765733f713d74626e3a414e64394763525a53346764767632364e384e37647072393270506f486d5650335251387a7464647261766a4a6c77487231537735664354
2018 - Tell Me Where to Look: Guided Attention Inference Networkhttps://arxiv.org/abs/1802.10171
https://camo.githubusercontent.com/d68e5b17632f32e6bc585fb750f09c9c6264b707680eefc4cc308ff2ca044e14/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f67726f756e6461692d7765622d70726f642f6d656469612532467573657273253246757365725f353531303825324670726f6a6563745f3838303930253246696d6167657325324678312e706e67
https://patch-diff.githubusercontent.com/axruff/DeepLearning#composition
2005 - Image Parsing: Unifying Segmentation, Detection, and Recognitionhttps://link.springer.com/article/10.1007/s11263-005-6642-x
2013 - Complexity of Representation and Inference in Compositional Models with Part Sharinghttps://arxiv.org/abs/1301.3560
2017 - Interpretable Convolutional Neural Networkshttps://arxiv.org/abs/1710.00935
https://camo.githubusercontent.com/80a343461a4b6f2aad6c2ded2bf18239670aac1429ad59e765df177e7854018a/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f323731322f302a444773306f314446484361434d5a7659
2019 - Local Relation Networks for Image Recognition https://arxiv.org/pdf/1904.11491.pdf
https://camo.githubusercontent.com/a1d5d65dea06ada0a5add8c5c55ae91ae1c8467b68e4bc520f7e3237469b2385/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f67726f756e6461692d7765622d70726f642f6d656469612532467573657273253246757365725f313038353925324670726f6a6563745f333536383334253246696d6167657325324678312e706e67
2017 - Teaching Compositionality to CNNshttps://www.semanticscholar.org/paper/Teaching-Compositionality-to-CNNs-Stone-Wang/3726b82007512a15a530fd1adad57af58a9abb62
https://camo.githubusercontent.com/55066120a183a199607c034378861dc7d6f4827ed80ce98fcc24999048319500/68747470733a2f2f7777772e7669636172696f75732e636f6d2f77702d636f6e74656e742f75706c6f6164732f323031372f31302f636f6d706f736974696f6e616c697479332e706e67
2020 - Concept Bottleneck Modelshttps://arxiv.org/abs/2007.04612
https://camo.githubusercontent.com/1485abc94cf60159da17fcd149da6635be4d074986371b0c6256e130f806f2a4/68747470733a2f2f696d616765732e6465657061692e6f72672f636f6e7665727465642d7061706572732f323030372e30343631322f666967757265732f7465617365722e706e67
https://patch-diff.githubusercontent.com/axruff/DeepLearning#capsule-networks
2017 - Dynamic Routing Between Capsuleshttps://arxiv.org/abs/1710.09829
https://camo.githubusercontent.com/f64736e4188bbe9a0d470f65b157aa5e288e1d443313b7969cc8d7ac7fd65af3/68747470733a2f2f63646e2d696d616765732d312e6d656469756d2e636f6d2f6669742f742f313630302f3438302f302a396676625f786153537157375856625f2e706e67
https://patch-diff.githubusercontent.com/axruff/DeepLearning#transformers
2020 - SURVEY: A Survey on Visual Transformerhttps://arxiv.org/abs/2012.12556
2021 - SURVEY: Transformers in Vision: A Surveyhttps://arxiv.org/abs/2101.01169
2023 - SURVEY: A Comprehensive Survey on Applications of Transformers for Deep Learning Taskshttps://paperswithcode.com/paper/a-comprehensive-survey-on-applications-of
https://patch-diff.githubusercontent.com/axruff/DeepLearning#3d-shape
2020 - [NeRF]: Representing Scenes as Neural Radiance Fields for View Synthesis https://arxiv.org/abs/2003.08934
https://camo.githubusercontent.com/b48c1aa978af157be5b81d9e96a6451cef1683dd00dfd832c804cffa9cbb82d5/68747470733a2f2f75706c6f6164732d73736c2e776562666c6f772e636f6d2f3531653064373364383364303662616137613030303030662f3565373030656636303637623433383231656435323736385f706970656c696e655f776562736974652d30312d702d3830302e706e67
2020 - [BLOG] NeRF Explosion 2020https://dellaert.github.io/NeRF/
2020 - [SURVEY] State of the Art on Neural Renderinghttps://arxiv.org/abs/2004.03805
https://camo.githubusercontent.com/9d31f9c2a2166685c04b4de24b2c85fd382cdad338caac8c1eb3e4a6cf2853c3/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f63386539306130352d333230372d343363632d396131362d3030626537626461653533362f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313032303525324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130323035543134323832345a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d3433326636303936613662393430636164616164653635623731646437643835363238653435363735363534383961336365643232623066643631363066353226582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
2020 - AutoInt: Automatic Integration for Fast Neural Volume Renderinghttps://arxiv.org/abs/2012.01714?s=09
https://camo.githubusercontent.com/58b044f4d1a49f50bbc03c3e0d29950aa9ee17eaefd6648c6155fbd87d4f35d7/68747470733a2f2f692e70696e696d672e636f6d2f353634782f63622f30302f64382f63623030643836373030626334653932363137306635623830643535303361322e6a7067
https://camo.githubusercontent.com/8c7935e79cd5c644e6fa18c02a72815bf80cde14383cf99581dc3fd0fad905d2/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f34326661633064312d313339362d343233392d386163652d6466333736303666353062362f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313032303825324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130323038543132343233315a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d6436616335643763633235626332613966663030613064623563646631666333313639353335336535333261306436623164303363396335333739336530313926582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
2020 - A Curvature and Density‐based Generative Representation of Shapeshttps://onlinelibrary.wiley.com/doi/full/10.1111/cgf.14094
https://camo.githubusercontent.com/8db4386da69e4976fb3cc169db1fae49ed354e8144e0852402cf78c9f02baae6/68747470733a2f2f692e70696e696d672e636f6d2f353634782f62372f38642f33352f62373864333531666663333265323234636163326632343362373032373565322e6a7067
2021 - Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networkshttps://paschalidoud.github.io/neural_parts
https://camo.githubusercontent.com/9f754d591c4caff380bcca22dca77f6a7570682c246d56fd76412a3c978bc71d/68747470733a2f2f7061736368616c69646f75642e6769746875622e696f2f70726f6a656374732f6e657572616c5f70617274732f6172636869746563747572652e706e67
https://patch-diff.githubusercontent.com/axruff/DeepLearning#logic-and-semantics
2019 - Neural Logic Machineshttps://arxiv.org/abs/1904.11694
https://camo.githubusercontent.com/37f9f7c462b662d1985f9a3ecaa83657ae99e80d851ebb4279f9ab280227abfb/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f66373830633565312d396164632d346561352d623835362d3837303931656336333663652f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313036313425324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130363134543039323334375a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d3433373664666333323937623166393939356264376232363561616435656639623164303636376632373537656632366433363237613237663438363332613426582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
https://patch-diff.githubusercontent.com/axruff/DeepLearning#optimization-and-regularisation
Random search for hyper-parameter optimisationhttp://www.jmlr.org/papers/v13/bergstra12a.html
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shifthttps://arxiv.org/pdf/1502.03167.pdf
[Adam]: A Method for Stochastic Optimizationhttps://arxiv.org/abs/1412.6980
[Dropout]: A Simple Way to Prevent Neural Networks from Overfittinghttp://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networkshttps://arxiv.org/abs/1406.6909
https://arxiv.org/abs/1511.07122https://arxiv.org/abs/1511.07122
https://user-images.githubusercontent.com/22321977/48708394-7121c980-ec3d-11e8-98ab-2c116df0aaae.png
2017 - The Marginal Value of Adaptive Gradient Methods in Machine Learninghttps://papers.nips.cc/paper/7003-the-marginal-value-of-adaptive-gradient-methods-in-machine-learning
https://arxiv.org/abs/1806.09055https://arxiv.org/abs/1806.09055
2018 - Tune: A Research Platform for Distributed Model Selection and Traininghttps://arxiv.org/abs/1807.05118
[github]https://github.com/ray-project/ray/tree/master/python/ray/tune
2017 - Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagationhttps://arxiv.org/abs/1602.05179
2017 - Understanding deep learning requires rethinking generalizationhttps://arxiv.org/abs/1611.03530
2018 - Error Forward-Propagation: Reusing Feedforward Connections to Propagate Errors in Deep Learninghttps://arxiv.org/abs/1808.03357
2018 - An Empirical Model of Large-Batch Traininghttps://arxiv.org/abs/1812.06162v1
https://camo.githubusercontent.com/d6b789b262639f8d58d1f96458c214f0fffa27f297e58591eaedfb5518012662/68747470733a2f2f692e70696e696d672e636f6d2f353634782f33362f62622f65342f33366262653464393531613163313030373134656137626161343365306534342e6a7067
2018 - A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decayhttps://arxiv.org/abs/1803.09820
2019 - Training Neural Networks with Local Error Signalshttps://arxiv.org/abs/1901.06656
[github]https://github.com/anokland/local-loss
2019 - Switchable Normalization for Learning-to-Normalize Deep Representationhttps://arxiv.org/abs/1907.10473
https://camo.githubusercontent.com/721ad55fd7370315007ce982591601debfb5f4e2761e4d36d4a36651175601ef/687474703a2f2f6c756f70696e672e6d652f706f73742f66616d696c792d6e6f726d616c697a6174696f6e2f534e2e706e67
2019 - Revisiting Small Batch Training for Deep Neural Networkshttps://arxiv.org/abs/1804.07612
2019 - Cyclical Learning Rates for Training Neural Networkshttps://arxiv.org/abs/1506.01186
https://camo.githubusercontent.com/b1a47c67f2a393ce64ab96081de676d212272581ab0f9b31184b92ab9344ec6b/68747470733a2f2f7777772e7079696d6167657365617263682e636f6d2f77702d636f6e74656e742f75706c6f6164732f323031392f30372f6b657261735f636c725f747269616e67756c6172322e706e67
2019 - DeepOBS: A Deep Learning Optimizer Benchmark Suitehttps://arxiv.org/abs/1903.05499
https://github.com/fsschneider/DeepOBS/raw/master/docs/deepobs_banner.png
2019 - A Recipe for Training Neural Networks. Andrey Karpathi Bloghttp://karpathy.github.io/2019/04/25/recipe/
2020 - Fantastic Generalization Measures and Where to Find Themhttps://arxiv.org/abs/1912.02178
2020 - Descending through a Crowded Valley -- Benchmarking Deep Learning Optimizershttps://arxiv.org/abs/2007.01547
https://user-images.githubusercontent.com/544269/95753705-f18fbe80-0cdc-11eb-9499-6bf22fa456e0.png
2020 - Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depthhttps://arxiv.org/abs/2010.15327
https://camo.githubusercontent.com/f53da56c5b941f8cd1eb7ab0d848a219e321eb58612c2f24b0514681e460b8a8/68747470733a2f2f692e70696e696d672e636f6d2f353634782f31352f38622f61662f31353862616633376561306236663035636330623064316664326633363464322e6a7067
2021 - Revisiting ResNets: Improved Training and Scaling Strategieshttps://arxiv.org/abs/2103.07579
https://patch-diff.githubusercontent.com/axruff/DeepLearning#pruning-and-compression
2013 - Do Deep Nets Really Need to be Deep?https://arxiv.org/abs/1312.6184
2015 - Learning both Weights and Connections for Efficient Neural Networkshttps://arxiv.org/abs/1506.02626
https://camo.githubusercontent.com/7587cca9f5787cfc952ecac98b1af8fa20f49c84bfdbb4a718857f85bfd6345b/68747470733a2f2f786d666269742e6769746875622e696f2f696d672f70617065722d7072756e696e672d6e6574776f726b2d64656d6f2e706e67
2015 - Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codinghttps://arxiv.org/abs/1510.00149
https://camo.githubusercontent.com/18317e42f153709acbbfd62cdf8ef13d3c017aa320dcc26ec02f134baeac152b/68747470733a2f2f616e616e646a2e696e2f77702d636f6e74656e742f75706c6f6164732f64632e706e67
2015 - Distilling the Knowledge in a Neural Networkhttps://arxiv.org/abs/1503.02531
2017 - Learning Efficient Convolutional Networks through Network Slimminghttps://arxiv.org/abs/1708.06519
[github]https://github.com/liuzhuang13/slimming
https://user-images.githubusercontent.com/8370623/29604272-d56a73f4-879b-11e7-80ea-0702de6bd584.jpg
2018 - Rethinking the Value of Network Pruninghttps://arxiv.org/abs/1810.05270
https://camo.githubusercontent.com/9bed56bebaa2e47706602c3a8ebb7fb8ff75e9c1300dbee22ec9aad4db1dc21e/68747470733a2f2f656e637279707465642d74626e302e677374617469632e636f6d2f696d616765733f713d74626e3a414e64394763545271394c6c6b6e464e6d4379586f4b6f455671664d58334a675036365435457a706268344646397855564c4255306a4f36
2018 - Slimmable Neural Networkshttps://arxiv.org/abs/1812.08928
https://user-images.githubusercontent.com/22609465/50390872-1b3fb600-0702-11e9-8034-d0f41825d775.png
2019 - Universally Slimmable Networks and Improved Training Techniqueshttps://arxiv.org/abs/1903.05134
https://user-images.githubusercontent.com/22609465/54562571-45b5ae00-4995-11e9-8984-49e32d07e325.png
2019 - The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networkshttps://arxiv.org/abs/1803.03635
https://camo.githubusercontent.com/f0c2a098b57ba8f790a75988d12f2898cae303b2f9dc121d882e8cc71f7ca856/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f323931362f312a4972614b6e6f77796b53794d5a74725731644a4f56412e706e67
2019 - AutoSlim: Towards One-Shot Architecture Search for Channel Numbershttps://arxiv.org/abs/1903.11728
https://camo.githubusercontent.com/258dab9e8ee3980bde288f1de918dd0eef0e55b3864272c79f30716f3af3f54e/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f67726f756e6461692d7765622d70726f642f6d656469612532467573657273253246757365725f313425324670726f6a6563745f333732323435253246696d6167657325324678312e706e67
https://patch-diff.githubusercontent.com/axruff/DeepLearning#analysis-and-interpretability
2015 - Visualizing and Understanding Recurrent Networkshttps://arxiv.org/abs/1506.02078
2016 - Discovering Causal Signals in Imageshttps://arxiv.org/abs/1605.08179
https://camo.githubusercontent.com/96e516edfccf674551c34591918a7c1ff48468a646cb07b5068b4d1e1ab07e86/68747470733a2f2f322e62702e626c6f6773706f742e636f6d2f2d5a53375748676f336639552f584432366964784e4545492f4141414141414141426c382f4469704a31466d335a4b304333745868753033707343346e4279546c49442d7351434c63424741732f73313630302f53637265656e25324253686f74253242323031392d30312d3135253242617425324231392e34382e31332e706e67
2016 - [Grad-CAM]: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localizationhttps://arxiv.org/abs/1610.02391
[github]https://github.com/jacobgil/pytorch-grad-cam
https://camo.githubusercontent.com/b904ef5c7f239aece9effb1d7a5473910d864f1d7cce3069ce09d6756fbcaba2/68747470733a2f2f656e637279707465642d74626e302e677374617469632e636f6d2f696d616765733f713d74626e3a414e6439476353523935454f5255755971786b334d74576969516f446d486e697a4856507872314a6e47566266574a724865734a6a5a6c6e2673
2017 - Visualizing the Loss Landscape of Neural Netshttps://arxiv.org/abs/1712.09913
https://github.com/tomgoldstein/loss-landscape/raw/master/doc/images/resnet56_noshort_small.jpg
2019 - [SURVEY] Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiershttps://ieeexplore.ieee.org/document/8371286
2018 - GAN Dissection: Visualizing and Understanding Generative Adversarial Networkshttps://arxiv.org/abs/1811.10597v1
https://camo.githubusercontent.com/093146e1e52259ab58bfcd7201ea371aedceb4371d99078dd791dc58f46dfef0/68747470733a2f2f692e70696e696d672e636f6d2f6f726967696e616c732f35612f64662f65392f35616466653937653835613930323364376631313439396162353765376461662e706e67
2018 Interactive toolhttps://gandissect.csail.mit.edu/
[Netron ] Visualizer for deep learning and machine learning modelshttps://github.com/lutzroeder/Netron
https://raw.githubusercontent.com/lutzroeder/netron/master/media/screenshot.png
2019 - [Distill]: Computing Receptive Fields of Convolutional Neural Networkshttps://distill.pub/2019/computing-receptive-fields/
2019 - On the Units of GANshttps://arxiv.org/abs/1901.09887
https://camo.githubusercontent.com/1c87876e9c57220cdea10738ee3b528a1617edd6b45f0108d8994b4baad55d81/68747470733a2f2f6e6575726f686976652e696f2f77702d636f6e74656e742f75706c6f6164732f323031382f31322f756e69742d64697374722d373730783338322e6a7067
2019 - Unmasking Clever Hans Predictors and Assessing What Machines Really Learnhttps://arxiv.org/abs/1902.10178
https://camo.githubusercontent.com/4acf1f1b195ad38cf0dea961ac23b8a32805e4477f88bff041e859bd4de4376b/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f37643236646365622d336361322d343033392d393263322d3066636237356637646266632f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313034323925324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130343239543133313631355a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d3431323833373131646361623032333533333061323532333237643632633733323234653733623635306131616236323838363436666234393134383561663226582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
2020 - Actionable Attribution Maps for Scientific Machine Learninghttps://arxiv.org/abs/2006.16533
https://camo.githubusercontent.com/a45d9541a761a1289dc2b3045bc8c1433eca1a53fe8ddafeb4cd5cef8c63cb8f/68747470733a2f2f692e70696e696d672e636f6d2f353634782f34352f62302f35312f34356230353130306266663836366239386666303530343333643465363464642e6a7067
2020 - Shortcut Learning in Deep Neural Networkshttps://arxiv.org/abs/2004.07780
https://camo.githubusercontent.com/bc42b53251312fe47c9c7b4234c2c8ebe128b65218c050bf8987e8a5ed4ba8ec/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f31663063383364342d316338612d343161612d383636342d3032383238393332626330632f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313034323925324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130343239543133313531355a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d6233303639613535636238363533326662643463616433613135656361303938383032346665646433353933626533313165313530633939313936623533313726582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
2021 - VIDEO: CVPR 2021 Workshop. Interpretable Neural Networks for Computer Vision: Clinical Decisions that are Aided, not Automatedhttps://www.youtube.com/watch?v=x7U5qC6eMnE
https://camo.githubusercontent.com/3aee79f9d1efb256483dce29003229f1d6d8ec5a3666affec091ad7d9afc21c0/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f33396232343734652d353933302d343839642d613231352d3161613531626534303638312f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313036323225324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130363232543131343333315a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d3733663034336136353332343965363532306133643737306566626265343834356632376464326337383432653439396164343039373434623031666236303626582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
2021 - VIDEO. CVPR 2021 Workshop. Interpreting Deep Generative Models for Interactive AI Content Creation by Bolei Zhou (CUHK)https://www.youtube.com/watch?v=PtRU2B6Iml4
https://camo.githubusercontent.com/6285e8f64de047cfff92a1bcef0dea500c3fcc66182c66c62b8b7bb4d21e74c2/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f39363261376465372d613431382d346438622d613162352d6530303930333461363530362f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313036323225324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130363232543131343832365a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d6233386161356263623161333030393335623261613036326434383430343432363731363830336366363330626132326330353531333062316139353338356326582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
https://patch-diff.githubusercontent.com/axruff/DeepLearning#tasks
https://patch-diff.githubusercontent.com/axruff/DeepLearning#segmentation
2019 - Panoptic Segmentationhttp://openaccess.thecvf.com/content_CVPR_2019/html/Kirillov_Panoptic_Segmentation_CVPR_2019_paper.html
https://camo.githubusercontent.com/ad055e0ce5f751a637f18a9c2a8dacdd61a186442c98b974a971c1442e41345d/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f313430302f312a4f656c5675763274685547416a5f34303057667365512e706e67
2019 - The Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentationhttps://arxiv.org/abs/1907.13236
https://camo.githubusercontent.com/123c3a364123a9686fcdaab2a74b963dcae2774ed0a5360b7d736f2ad6cb4e6d/68747470733a2f2f692e70696e696d672e636f6d2f353634782f33312f61372f61312f33316137613161373062643736653033356439326638313163623437303164302e6a7067
2019 - ShapeMask: Learning to Segment Novel Objects by Refining Shape Priorshttps://arxiv.org/abs/1904.03239
https://camo.githubusercontent.com/d790cd6f034d80c4b91382fd29efe4d9bb1d6cb8d744344d6db2f7ffde385e4c/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f67726f756e6461692d7765622d70726f642f6d656469612f75736572732f757365725f3232353131342f70726f6a6563745f3335303434342f696d616765732f666967757265732f73686170656d61736b5f666967315f76332e6a7067
2019 - Learning to Segment via Cut-and-Pastehttps://arxiv.org/abs/1803.06414
https://camo.githubusercontent.com/c51a6984f4c7cb354167099255b31fc7ec758e474b694ed7e2d79b093f258c56/68747470733a2f2f6d656469612e737072696e6765726e61747572652e636f6d2f6f726967696e616c2f737072696e6765722d7374617469632f696d6167652f63687025334131302e313030372532463937382d332d3033302d30313233342d325f332f4d656469614f626a656374732f3437343231325f315f456e5f335f466967335f48544d4c2e676966
2019 - YOLACT Real-time Instance Segmentationhttps://arxiv.org/abs/1904.02689
[github]https://github.com/dbolya/yolact
https://camo.githubusercontent.com/90e000c75a1d31b1ed2436d4b5ec017c8ee0810de36bdfa0ec409ef851c22a61/68747470733a2f2f692e70696e696d672e636f6d2f353634782f35322f30632f33652f35323063336565356530363935343832633132613733653039366464346239662e6a7067
2021 - Boundary IoU: Improving Object-Centric Image Segmentation Evaluationhttps://arxiv.org/abs/2103.16562
[github]https://bowenc0221.github.io/boundary-iou/
https://camo.githubusercontent.com/d8fd1f179273aba1b85b58f8a9b83691265e57f24b8d059d90b593f6d6261046/68747470733a2f2f626f77656e63303232312e6769746875622e696f2f626f756e646172792d696f752f626f756e646172795f696f752e706e67
https://patch-diff.githubusercontent.com/axruff/DeepLearning#instance-segmentation
2017 - Mask R-CNNhttps://arxiv.org/abs/1703.06870v3
https://camo.githubusercontent.com/eb8df75ef19b948a40fee6274532c6b8de84369e461c0e478a7f00caf3178b7a/68747470733a2f2f70617065727377697468636f64652e636f6d2f6d656469612f6d6574686f64732f53637265656e5f53686f745f323032302d30352d32335f61745f372e34342e33345f504d2e706e67
2019 - Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidthhttps://arxiv.org/abs/1906.11109
githubhttps://github.com/axruff/SpatialEmbeddings
https://github.com/axruff/SpatialEmbeddings/raw/master/static/teaser.jpg
https://patch-diff.githubusercontent.com/axruff/DeepLearning#interactive-segmentation
2020 - Continuous Adaptation for Interactive Object Segmentation by Learning from Correctionshttps://arxiv.org/abs/1911.12709
https://camo.githubusercontent.com/34b132fd96252cbef98d61e52e3265e7f28f7836f0cd0fe5fe8ff9ddb7d1785a/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f66633063656437652d626561662d346565342d396663652d3930656530663964333163302f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313036313725324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130363137543038313832395a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d6666633164383963633064636564323230613261303061653433353132316237666162653763336661656236653131353566373137313339656265326233353726582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
https://patch-diff.githubusercontent.com/axruff/DeepLearning#anomaly-detection
2009 - Anomaly Detection: A Surveyhttps://www.vs.inf.ethz.ch/edu/HS2011/CPS/papers/chandola09_anomaly-detection-survey.pdf
2017 - Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoveryhttps://arxiv.org/abs/1703.05921v1
https://patch-diff.githubusercontent.com/axruff/DeepLearning#semantic-correspondence
2017 - End-to-end weakly-supervised semantic alignmenthttps://arxiv.org/abs/1712.06861
https://camo.githubusercontent.com/c05b4ff567b7341240ebc406ae37739f31e41aea17e0e497d530dcabd2f7cd54/687474703a2f2f7777772e64692e656e732e66722f77696c6c6f772f72657365617263682f7765616b616c69676e2f696d616765732f7465617365722e6a7067
2019 - SFNet: Learning Object-aware Semantic Correspondencehttps://arxiv.org/abs/1904.01810
[github]https://github.com/cvlab-yonsei/SFNet
https://camo.githubusercontent.com/82812491c0093cc3342b6840899002b8295c62b279911fdbe86ebb52a0a24d6e/68747470733a2f2f63766c61622e796f6e7365692e61632e6b722f70726f6a656374732f53464e65742f53464e65745f66696c65732f7465617365722e706e67
2020 - Deep Semantic Matching with Foreground Detection and Cycle-Consistencyhttps://arxiv.org/abs/2004.00144
https://camo.githubusercontent.com/d9bbf184313e4a9e3e9d6319744569b9bf01b22aec80a14b4fa277f222ec79df/68747470733a2f2f692e70696e696d672e636f6d2f353634782f65392f37312f32302f65393731323030313236653032633836663861633263653334396465643930652e6a7067
https://patch-diff.githubusercontent.com/axruff/DeepLearning#optical-flow
2019 - SelFlow: Self-Supervised Learning of Optical Flowhttps://arxiv.org/abs/1904.09117
- [github]https://github.com/ppliuboy/SelFlow
https://camo.githubusercontent.com/f3906bff0202cc7260ff13888028ff31d9f34d4d038d65ed808bc4de12bfe94b/68747470733a2f2f692e70696e696d672e636f6d2f353634782f38302f38372f37342f38303837373432326433356166613161613137666536656564663665616166362e6a7067
2021 - AutoFlow: Learning a Better Training Set for Optical Flowhttp://people.csail.mit.edu/celiu/pdfs/CVPR21_AutoFlow.pdf
https://camo.githubusercontent.com/008fcc60b1bfaec7555d7b72c1eebc9744239556d013f7e0025819aa78a35cb7/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f31353731616137612d626666382d346537382d613834332d3137306532653666343365332f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313034323925324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130343239543037343631375a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d6461643330346434393137373637346563393362396236366564663635383338376636383439363539616563636534303765303433346438653934373738616526582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
2021 - SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full-Image Warpinghttps://arxiv.org/abs/2105.07014
https://camo.githubusercontent.com/8342a257fe08a534d4c4616070f63719b7445b70090e7a9e143a0ede6857584c/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f36323333346362642d396664342d343563342d616262612d6632653230616332666236632f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313037313325324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130373133543136323535365a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d3461663332646466383361323436313565643061663633313561656266616130326166626236396461336161306364636361656436353332383130653639656326582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
https://patch-diff.githubusercontent.com/axruff/DeepLearning#methods
https://patch-diff.githubusercontent.com/axruff/DeepLearning#transfer-learning
Transfer Learninghttps://github.com/axruff/TransferLearning
Domain Adaptationhttps://github.com/axruff/TransferLearning
Domain Randomizationhttps://github.com/axruff/TransferLearning#domain-randomization
Style Transferhttps://github.com/axruff/TransferLearning#style-transfer
https://patch-diff.githubusercontent.com/axruff/DeepLearning#generative-modelling
Generative Modelshttps://github.com/axruff/TransferLearning#generative-models
https://patch-diff.githubusercontent.com/axruff/DeepLearning#weakly-supervised
2015 - Constrained Convolutional Neural Networks for Weakly Supervised Segmentationhttps://arxiv.org/abs/1506.03648
https://camo.githubusercontent.com/ffe64f9a55f2ffbd914e9f0d83807023b592b2e6b0e3878e30433c7122dc7400/68747470733a2f2f70656f706c652e656563732e6265726b656c65792e6564752f7e70617468616b2f696d616765732f6963637631352e706e67
2018 - Deep Learning with Mixed Supervision for Brain Tumor Segmentationhttps://arxiv.org/abs/1812.04571
https://camo.githubusercontent.com/a8eeda72b355dd3dfb02cd77f7d5a7633ae11011031efa7872f79259a7086e40/68747470733a2f2f7777772e737069656469676974616c6c6962726172792e6f72672f436f6e74656e74496d616765732f4a6f75726e616c732f4a4d494f42552f362f332f3033343030322f576562496d616765732f4a4d495f365f335f3033343030325f663030312e706e67
2019 - Localization with Limited Annotation for Chest X-rayshttps://arxiv.org/abs/1909.08842v1
https://camo.githubusercontent.com/f7729d137f8225d1c2f80a2958eb6dfb2c19d49c174aa12056445d3eb72821ad/68747470733a2f2f656e637279707465642d74626e302e677374617469632e636f6d2f696d616765733f713d74626e3a414e64394763544f46617862786277754b6c6e36536762465657795650324137746a2d4354516530356973564b483367623149477167383469672673
2019 - Doubly Weak Supervision of Deep Learning Models for Head CThttps://jdunnmon.github.io/miccai_crc.pdf
https://camo.githubusercontent.com/06862febfd9efc41bc8c2fea6ebb02ebb56a2dc49bb97c31e1694a3039a3e8b9/68747470733a2f2f6d656469612e737072696e6765726e61747572652e636f6d2f6f726967696e616c2f737072696e6765722d7374617469632f696d6167652f63687025334131302e313030372532463937382d332d3033302d33323234382d395f39302f4d656469614f626a656374732f3439303237375f315f456e5f39305f466967325f48544d4c2e706e67
2019 - Training Complex Models with Multi-Task Weak Supervisionhttps://www.ncbi.nlm.nih.gov/pubmed/31565535
https://camo.githubusercontent.com/456de6bdd5cfca42294262771cf5d435d6d9a420de4e880dbd9e48d5b9d42902/68747470733a2f2f7777772e6e6362692e6e6c6d2e6e69682e676f762f706d632f61727469636c65732f504d43363736353336362f62696e2f6e69686d732d313033373634332d66303030312e6a7067
2020 - Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methodshttps://arxiv.org/abs/2002.11955
https://patch-diff.githubusercontent.com/axruff/DeepLearning#semi-supervised
2014 - Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networkshttps://arxiv.org/abs/1406.6909
https://camo.githubusercontent.com/38f4d146f7b55c17d1677ed146d1cce328fcd1c4f21a1f28e119ed59649cae3d/68747470733a2f2f7777772e696e666572656e63652e76632f636f6e74656e742f696d616765732f323031372f30352f53637265656e2d53686f742d323031372d30352d31312d61742d392e33312e33372d414d2e706e67
2017 - Random Erasing Data Augmentationhttps://arxiv.org/abs/1708.04896v2
[github]https://github.com/zhunzhong07/Random-Erasing
https://github.com/zhunzhong07/Random-Erasing/raw/master/all_examples-page-001.jpg
2017 - Smart Augmentation - Learning an Optimal Data Augmentation Strategyhttps://arxiv.org/abs/1703.08383
2017 - Population Based Training of Neural Networkshttps://arxiv.org/abs/1711.09846
2018 - [Survey]: Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysishttps://arxiv.org/abs/1804.06353
2018 - Albumentations: fast and flexible image augmentationshttps://arxiv.org/abs/1809.06839
[github]https://github.com/albu/albumentations
2018 - Data Augmentation by Pairing Samples for Images Classificationhttps://arxiv.org/abs/1801.02929
2018 - [AutoAugment]: Learning Augmentation Policies from Datahttps://arxiv.org/abs/1805.09501
2018 - Synthetic Data Augmentation using GAN for Improved Liver Lesion Classificationhttps://arxiv.org/abs/1801.02385
2018 - GAN Augmentation: Augmenting Training Data using Generative Adversarial Networkshttps://arxiv.org/abs/1810.10863
2019 - [UDA]: Unsupervised Data Augmentation for Consistency Traininghttps://arxiv.org/abs/1904.12848
[github]https://github.com/google-research/uda
https://camo.githubusercontent.com/0896cb65f9a87983bee3f2f71f3c064c33216413/68747470733a2f2f692e696d6775722e636f6d2f4c38476b3634622e706e67
2019 - [MixMatch]: A Holistic Approach to Semi-Supervised Learninghttps://arxiv.org/abs/1905.02249
https://camo.githubusercontent.com/47d3e6a3791ced26172ed16f595a2476d7a16aaec337f2cbc83cc069cfcdba59/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f313430322f312a69344f66587a74696843586772785235325a6c6f77512e706e67
2019 - [RealMix]: Towards Realistic Semi-Supervised Deep Learning Algorithmshttps://arxiv.org/abs/1912.08766v1
https://camo.githubusercontent.com/87747655d03f4046e3c46046a56b9fd6f251eb69038df1a4e79f65c7dd546602/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f67726f756e6461692d7765622d70726f642f6d656469612f75736572732f757365725f31342f70726f6a6563745f3430323431312f696d616765732f5265616c4d69782e706e67
2019 - Population Based Augmentation: Efficient Learning of Augmentation Policy Scheduleshttps://arxiv.org/abs/1905.05393
[github]https://github.com/arcelien/pba
2019 - [AugMix]: A Simple Data Processing Method to Improve Robustness and Uncertaintyhttps://arxiv.org/abs/1912.02781v1
[github]https://github.com/google-research/augmix
https://camo.githubusercontent.com/ba93041b1e63b9e939c88ec6a944eb83e92ec78fa7faa937884f95b1c7abaae9/68747470733a2f2f707974686f6e617765736f6d652e636f6d2f636f6e74656e742f696d616765732f323031392f31322f4175674d69782e6a7067
2019 - Self-training with [Noisy Student] improves ImageNet classificationhttps://arxiv.org/abs/1911.04252
https://camo.githubusercontent.com/4807478c1cc9a9d801ae6ccc856a31664c0da1cd3282b4f84bc0c66aa4ae168a/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f67726f756e6461692d7765622d70726f642f6d656469612532467573657273253246757365725f323337383225324670726f6a6563745f333937363037253246696d6167657325324678312e706e67
2020 - Rain rendering for evaluating and improving robustness to bad weatherhttps://arxiv.org/abs/2009.03683
https://camo.githubusercontent.com/22f862a13b1e4ea956b171c2d0c4f9a716d3a7d491960b7b1b6be6023a472309/68747470733a2f2f6d656469612e737072696e6765726e61747572652e636f6d2f6c773638352f737072696e6765722d7374617469632f696d6167652f61727425334131302e313030372532467331313236332d3032302d30313336362d332f4d656469614f626a656374732f31313236335f323032305f313336365f46696731335f48544d4c2e706e67
https://patch-diff.githubusercontent.com/axruff/DeepLearning#unsupervised-learning
2015 - Unsupervised Visual Representation Learning by Context Predictionhttps://arxiv.org/abs/1505.05192
https://camo.githubusercontent.com/89d96d45874ccba7f150c0fb2d0c9f48563b16d27bd8dca5ae8f37cd2955fc4a/68747470733a2f2f6461766964737475747a2e64652f776f726470726573732f77702d636f6e74656e742f75706c6f6164732f323031372f30332f646f65727363682e6a7067
2016 - Colorful Image Colorizationhttps://arxiv.org/abs/1603.08511
https://camo.githubusercontent.com/596ed710940a0516cec0cca14ab36fd39db71165ae5aa5d1f7c9272bb1d51b77/68747470733a2f2f726963687a68616e672e6769746875622e696f2f636f6c6f72697a6174696f6e2f7265736f75726365732f696d616765732f6e65745f6469616772616d2e6a7067
2016 - Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzleshttps://arxiv.org/abs/1603.09246
https://camo.githubusercontent.com/d71ea9719a7c249164f20ca8c4163174e7fd7874bf28414f3e56830da3abd415/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f39373666656631652d633466652d343539632d383662322d3335333838313465353932342f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313036323125324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130363231543039313030395a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d3530323231323338663339353531336632303532616664613536303965383037636132393334373430313865633865383764373562656365383135373931616326582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
2016 - Context Encoders: Feature Learning by Inpaintinghttps://www.semanticscholar.org/paper/Context-Encoders%3A-Feature-Learning-by-Inpainting-Pathak-Kr%C3%A4henb%C3%BChl/7d0effebfa4bed19b6ba41f3af5b7e5b6890de87
https://camo.githubusercontent.com/b8a3f7774b0ad61376bb52d51fad02020d4cb70fe5f1b75a97f3838c87e3c35e/68747470733a2f2f692e70696e696d672e636f6d2f353634782f63312f32612f39622f63313261396262333466303438353331646430383666393730366434333036662e6a7067
2018 - Unsupervised Representation Learning by Predicting Image Rotationshttps://www.semanticscholar.org/paper/Unsupervised-Representation-Learning-by-Predicting-Gidaris-Singh/aab368284210c1bb917ec2d31b84588e3d2d7eb4
https://camo.githubusercontent.com/782f056e82a8d6c9d2041756fb06fbb5444008e4db88593841b6f34b99e1a8a6/68747470733a2f2f6d656469612e61727869762d76616e6974792e636f6d2f72656e6465722d6f75747075742f343634393632302f78312e706e67
2019 - Greedy InfoMax for Biologically Plausible Self-Supervised Representation Learninghttps://arxiv.org/abs/1905.11786
2019 - Unsupervised Learning via Meta-Learninghttps://arxiv.org/abs/1810.02334
2019 - [PIRL]: Self-Supervised Learning of Pretext-Invariant Representationshttps://www.semanticscholar.org/paper/Self-Supervised-Learning-of-Pretext-Invariant-Misra-Maaten/0170bb0b524df2c81b5adc3062c6001a2eb34c96
https://camo.githubusercontent.com/257534e606fcc6d190806f1d7704026edb643bddd7afabea3999d67057851e9c/68747470733a2f2f692e70696e696d672e636f6d2f353634782f30342f38322f38342f30343832383465666334386639613632353263643338393161303634306265332e6a7067
2019 - Representation Learning with Contrastive Predictive Codinghttps://arxiv.org/abs/1807.03748
2019 - [MoCo]: Momentum Contrast for Unsupervised Visual Representation Learninghttps://arxiv.org/abs/1911.05722
https://camo.githubusercontent.com/486da555bb9796aeda99e80ba495aacfae00def8cae248078f607d2e3df27ea5/68747470733a2f2f707974686f6e617765736f6d652e636f6d2f636f6e74656e742f696d616765732f323032302f30332f4d6f436f2e706e67
2019 - Self-supervised Visual Feature Learning with Deep Neural Networks: A Surveyhttps://arxiv.org/abs/1902.06162
2020 - [SimCLR]: A Simple Framework for Contrastive Learning of Visual Representationshttps://arxiv.org/abs/2002.05709
https://camo.githubusercontent.com/9bb8b8b359be4d0e0bdb2579076e84cf07e0e1f01b79f8c330d7b7c5e062eb80/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f383330302f312a317561413174453550446e5670536c6a785354456f512e706e67
2020 - ::SURVEY:: Self-supervised Visual Feature Learning with Deep Neural Networks: A Surveyhttps://arxiv.org/abs/1902.06162
2020 - [NeurIPS 2020 Workshop]: Self-Supervised Learning - Theory and Practicehttps://sslneuips20.github.io/pages/Accepted%20Paper.html
2020 - [BYOL]: Bootstrap Your Own Latent: A New Approach to Self-Supervised Learninghttps://www.semanticscholar.org/paper/Bootstrap-Your-Own-Latent%3A-A-New-Approach-to-Grill-Strub/38f93092ece8eee9771e61c1edaf11b1293cae1b
https://camo.githubusercontent.com/809fa657e1b2e3d669ce004e454d4b1897dff7f79ea3f587ca07574dce457fa3/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f38373966346361312d386632352d346633322d383730312d6261636231626439373263352f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313036323125324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130363231543039343735305a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d3332643537393532656331393035323434373836313963383366346566653035353262353863386637363437326563326230326565343538306436333763616526582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
2021 - [POST] Facebook: Self-supervised learning: The dark matter of intelligencehttps://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/
https://camo.githubusercontent.com/166f2ce7759a56473b24656e38377c25b248de289ca6f469448dab2a7734ba18/68747470733a2f2f73636f6e74656e742d667274332d312e78782e666263646e2e6e65742f762f7433392e323336352d362f3134383935343132355f3436313736313131383430353937395f323033353931343037353839333539363831305f6e2e706e673f5f6e635f6361743d313037266363623d312d33265f6e635f7369643d616438613964265f6e635f6f68633d324a62477357794a55316f41585f7641677259265f6e635f68743d73636f6e74656e742d667274332d312e7878266f683d3539376135653233376563393766616233323061343364656135653838356535266f653d3630463439313939
2021 - Task Fingerprinting for Meta Learning in Biomedical Image Analysishttps://www.semanticscholar.org/paper/Task-Fingerprinting-for-Meta-Learning-in-Biomedical-Godau-Maier-Hein/b3b7433817380b98951d4a6502a889b8ce8c7422
https://camo.githubusercontent.com/251b856fd62854afd7b1285569427b8acf37433a47534ace215358c82eea3109/68747470733a2f2f692e70696e696d672e636f6d2f353634782f62662f33382f64332f62663338643337633565643534313439373365373631326233366431336238612e6a7067
https://patch-diff.githubusercontent.com/axruff/DeepLearning#mutual-learning
2017 - Deep Mutual Learninghttps://arxiv.org/abs/1706.00384
https://camo.githubusercontent.com/384ab493554b652442096b1b1a3a7ae60cf0f6a14e313428d9dc2748de59a054/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f67726f756e6461692d7765622d70726f642f6d656469612f75736572732f757365725f313938392f70726f6a6563745f3130373435322f696d616765732f78312e706e67
2019 - Feature Fusion for Online Mutual Knowledge Distillation https://arxiv.org/abs/1904.09058
https://camo.githubusercontent.com/a9390a844632ff2ca9d015967576df0f06c6dca7157710cb296ea2030e616609/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f67726f756e6461692d7765622d70726f642f6d656469612532467573657273253246757365725f32323838383725324670726f6a6563745f333535353637253246696d616765732532466f766572616c6c70726f636573732e706e67
https://patch-diff.githubusercontent.com/axruff/DeepLearning#multitask-learning
2016 - Cross-Stitch Networks for Multi-task Learninghttps://www.semanticscholar.org/paper/Cross-Stitch-Networks-for-Multi-task-Learning-Misra-Shrivastava/f14325ec3041a73118bc4d819204cbbca07d5a71
https://camo.githubusercontent.com/6279bcdabd6f9504224fc49b412612916ff1fc0662baf9b0db4b6feb8e4351da/68747470733a2f2f692e70696e696d672e636f6d2f353634782f63362f30642f61652f63363064616539633338346666356431613233386164386665366666633362362e6a7067
2017 - An Overview of Multi-Task Learning in Deep Neural Networkshttps://arxiv.org/abs/1706.05098
2017 - Multi-task Self-Supervised Visual Learninghttps://arxiv.org/abs/1708.07860
https://patch-diff.githubusercontent.com/axruff/DeepLearning#reinforcement-learning
https://arxiv.org/abs/1811.12560https://arxiv.org/abs/1811.12560
https://arxiv.org/abs/1810.06339https://arxiv.org/abs/1810.06339
https://arxiv.org/pdf/1312.5602.pdfhttps://arxiv.org/pdf/1312.5602.pdf
https://spinningup.openai.com/en/latest/spinningup/keypapers.htmlhttps://spinningup.openai.com/en/latest/spinningup/keypapers.html
https://arxiv.org/abs/1406.6247https://arxiv.org/abs/1406.6247
https://raw.githubusercontent.com/torch/torch.github.io/master/blog/_posts/images/rva-diagram.png
https://patch-diff.githubusercontent.com/axruff/DeepLearning#inverse-reinforcement-learning
2019 - On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inferencehttps://arxiv.org/abs/1906.09624
https://camo.githubusercontent.com/ca395f3070779f32cddfb4eb6ec72e1f70d739ee865e9718963794d944b421c5/68747470733a2f2f692e70696e696d672e636f6d2f353634782f63662f30612f30382f63663061303835396361373439643338396335636363323464323066643161332e6a7067
https://patch-diff.githubusercontent.com/axruff/DeepLearning#visual-questions-and-object-retrieval
2015 - Natural Language Object Retrievalhttps://arxiv.org/abs/1511.04164
2019 - CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressionshttps://arxiv.org/abs/1901.00850
https://patch-diff.githubusercontent.com/axruff/DeepLearning#datasets
[ADE20K Dataset]: Semantic Segmentation [website]http://groups.csail.mit.edu/vision/datasets/ADE20K/
http://people.csail.mit.edu/bzhou/publication/scene-parse-camera-ready.pdfhttp://people.csail.mit.edu/bzhou/publication/scene-parse-camera-ready.pdf
https://camo.githubusercontent.com/8fc3eeecfc90ada67d112cac7cd1f139236a17515888ae5fcce37fe9c6476414/687474703a2f2f67726f7570732e637361696c2e6d69742e6564752f766973696f6e2f64617461736574732f41444532304b2f6173736574732f696d616765732f6578616d706c65732e706e67
[OPENSURFACES]: A Richly Annotated Catalog of Surface Appearancehttp://opensurfaces.cs.cornell.edu/
https://www.cs.cornell.edu/~paulu/opensurfaces.pdfhttps://www.cs.cornell.edu/~paulu/opensurfaces.pdf
https://camo.githubusercontent.com/afcbd38c975f8f8448179e287467374dd93905fa2c55a18a9acfe3cfb6b62f4d/687474703a2f2f6f70656e73757266616365732e63732e636f726e656c6c2e6564752f7374617469632f696d672f746561736572342d7765622e6a7067
[ShapeNet] - a richly-annotated, large-scale dataset of 3D shapes [website] https://www.shapenet.org/
https://camo.githubusercontent.com/ea9ea08491f3759d931af14cf36a22fbc47d494e15113435d743df41b54d3722/68747470733a2f2f7777772e73686170656e65742e6f72672f7265736f75726365732f696d616765732f6c6f676f2e706e67
ShapeNet: An Information-Rich 3D Model Repositoryhttps://arxiv.org/abs/1512.03012
Beyond PASCAL: A Benchmark for 3D Object Detection in the Wild [website]http://cvgl.stanford.edu/projects/pascal3d.html
[paper]https://ieeexplore.ieee.org/document/6836101
[ObjectNet3D]: A Large Scale Database for 3D Object Recognitionhttp://cvgl.stanford.edu/projects/objectnet3d/
https://camo.githubusercontent.com/dfdef0a02b98ff2c8f8685dc2ac11ead8b230577165672197985162554251c60/687474703a2f2f6376676c2e7374616e666f72642e6564752f70726f6a656374732f6f626a6563746e657433642f4f626a6563744e657433442e706e67
[ModelNet]: a comprehensive clean collection of 3D CAD models for objects [website]http://modelnet.cs.princeton.edu/
https://camo.githubusercontent.com/03d2b9141b8e9d1a3ad89dbc7cb8cb28faf451e4962ac180ab6db60c65c51e34/687474703a2f2f3364766973696f6e2e7072696e6365746f6e2e6564752f70726f6a656374732f323031342f4d6f64656c4e65742f7468756d626e61696c2e6a7067
[3D ShapeNets]: A Deep Representation for Volumetric Shapes (2015)https://ieeexplore.ieee.org/document/7298801
[BLEND SWAP]: IS A COMMUNITY OF PASSIONATE BLENDER ARTISTS WHO SHARE THEIR WORK UNDER CREATIVE COMMONS LICENSEShttps://www.blendswap.com/
[DTD]: Describable Textures Datasethttps://www.robots.ox.ac.uk/~vgg/data/dtd/
https://camo.githubusercontent.com/5b2ccf34997e37aea89989d6ccb0c414336b4b101dace5d53a6efbacbcf0ee8d/68747470733a2f2f692e70696e696d672e636f6d2f353634782f31362f33652f65302f31363365653037366439366566313962663566376232343164343262363066392e6a7067
[MegaDepth]: Learning Single-View Depth Prediction from Internet Photoshttps://research.cs.cornell.edu/megadepth/
https://camo.githubusercontent.com/00bbb781ac3be41aa3ef0fe426ef0220c00bf97f2d97e8c2346a14d573f993c1/68747470733a2f2f72657365617263682e63732e636f726e656c6c2e6564752f6d65676164657074682f64656d6f322e706e67
Microsoft [COCO]: Common Objects in Contexthttps://arxiv.org/abs/1405.0312
[website]https://cocodataset.org/#home
https://camo.githubusercontent.com/cb6f3cdb8d918e11948a57e9ab5ddc9d76152fea9eeb788478b0efd519c8ddd0/68747470733a2f2f63646e2e736c696465736861726563646e2e636f6d2f73735f7468756d626e61696c732f636f636f646174617365742d3139303431303035333331362d7468756d626e61696c2d342e6a70673f63623d31353534383734343330
2020 - [CARLA] Open-source simulator for autonomous driving research.https://carla.org/
https://github.com/carla-simulator/carla/raw/master/Docs/img/video_thumbnail_0910.jpg
A Browsable Petascale Reconstruction of the Human Cortexhttps://ai.googleblog.com/2021/06/a-browsable-petascale-reconstruction-of.html
https://camo.githubusercontent.com/58418adf010a5bd00b1936b019f368e315db43f4f32350a03d4548fee1318967/68747470733a2f2f312e62702e626c6f6773706f742e636f6d2f2d4c484e537a704451734e672f594c5a65714f69586564492f41414141414141414870732f3936537469476f41646249416768756a45456e64397a546b696d64696776395541434c63424741735948512f773634302d683233302f696d616765352e706e67
2021 - Medical Segmentation Decathlon. Generalisable 3D Semantic Segmentationhttp://medicaldecathlon.com/
https://camo.githubusercontent.com/f4b7d781cd2b7344fe241422b9ec0eca80dd8b03cb4bb47aeb6858a13b8754fc/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f63383964363964662d343632332d346563622d623166342d3838386533623138303465362f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313036313625324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130363136543037353434335a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d3839373433366535353731643831626532376165663864346238373061363630666365353539333134343130396535343039656162613239373364323361316426582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
https://patch-diff.githubusercontent.com/axruff/DeepLearning#benchmarks
MLPerf: A broad ML benchmark suite for measuring performance of ML software frameworks, ML hardware accelerators, and ML cloud platforms.https://mlperf.org/results/
DAWNBench: is a benchmark suite for end-to-end deep learning training and inference.https://dawn.cs.stanford.edu/benchmark/
DAWNBench: An End-to-End Deep Learning Benchmark and Competition (paper) (2017)https://dawn.cs.stanford.edu/benchmark/papers/nips17-dawnbench.pdf
https://patch-diff.githubusercontent.com/axruff/DeepLearning#applications
http://opensurfaces.cs.cornell.edu/publications/minc/http://opensurfaces.cs.cornell.edu/publications/minc/
https://ge.in.tum.de/publications/tempogan/https://ge.in.tum.de/publications/tempogan/
https://camo.githubusercontent.com/349896d2786a7549921f64c4ec9addb59b6dc2117a91d1b5b94c40e1782d1d2c/68747470733a2f2f67652e696e2e74756d2e64652f77702d636f6e74656e742f75706c6f6164732f323031382f30322f7465617365722d31303830783336382e6a7067
BubGAN: Bubble Generative Adversarial Networks for Synthesizing Realistic Bubbly Flow Imageshttps://arxiv.org/abs/1809.02266
https://camo.githubusercontent.com/6474b8df78222f82620f665be59546f941d7376bd3e9c0b7588de61a90898177/68747470733a2f2f692e70696e696d672e636f6d2f353634782f36312f36342f63622f36313634636265313130346437633338663036333037633734646135633134652e6a7067
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping (2018)https://ieeexplore.ieee.org/abstract/document/8460875
https://camo.githubusercontent.com/d47fa1e509130fc13f6688a35b2f13c3e1cf2ccd9905549b82b91772625e5e4f/68747470733a2f2f7777772e616c6578697270616e2e636f6d2f7075626c69632f73696d327265616c6772617370696e672f696d6167652d636f6d70617269736f6e2e706e67
https://patch-diff.githubusercontent.com/axruff/DeepLearning#applications-medical-imaging
https://doi.org/10.1117/12.2293755https://doi.org/10.1117/12.2293755
https://github.com/axruff/ML_papers/raw/master/images/1902.png
Deep learning with domain adaptation for accelerated projection‐reconstruction MR (2017)https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.27106
Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification (2018)https://arxiv.org/abs/1801.02385
GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks (2018)https://arxiv.org/abs/1810.10863
Abdominal multi-organ segmentation with organ-attention networks and statistical fusion (2018)https://arxiv.org/abs/1804.08414
Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation (2019)https://arxiv.org/abs/1904.06346
https://camo.githubusercontent.com/b33fb9011874e78acd7d4d7f582d9c4f17358c621bfd5240e4e6d56518082c08/68747470733a2f2f7777772e67726f756e6461692e636f6d2f6d656469612f61727869765f70726f6a656374732f3533303534332f78322e706e67
https://camo.githubusercontent.com/894d1e1ddc40adf1b977d475843cf04b2c60183cf18d675db3f3cd860ea99ff5/68747470733a2f2f6172732e656c732d63646e2e636f6d2f636f6e74656e742f696d6167652f312d73322e302d53313336313834313531383330323532342d6772312e6a7067
Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural Network (2018)https://arxiv.org/abs/1809.01687
2019 - H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumeshttps://arxiv.org/abs/1709.07330v3
https://camo.githubusercontent.com/7840da2423a491865a02e45cd2ed1a6630238071763bb2a12d1d17e4c2a0278b/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f323030302f312a75706162474876534a44766138775663743231684e672e706e67
2020 - [TorchIO]: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learninghttps://arxiv.org/abs/2003.04696
[github]https://github.com/fepegar/torchio
2020 - Reconstructing lost BOLD signal in individual participants using deep machine learninghttps://www.nature.com/articles/s41467-020-18823-9#disqus_thread
https://camo.githubusercontent.com/a744f7ff668c466968e8cdc48e1056ebc1464b730ed0401dc466e4c1d542909d/68747470733a2f2f6d656469612e737072696e6765726e61747572652e636f6d2f6c773638352f737072696e6765722d7374617469632f696d6167652f61727425334131302e313033382532467334313436372d3032302d31383832332d392f4d656469614f626a656374732f34313436375f323032305f31383832335f466967315f48544d4c2e706e673f61733d77656270
https://patch-diff.githubusercontent.com/axruff/DeepLearning#applications-x-ray-imaging
https://www.nature.com/articles/s41598-018-19426-7https://www.nature.com/articles/s41598-018-19426-7
2019 - Deep learning optoacoustic tomography with sparse datahttps://www.nature.com/articles/s42256-019-0095-3
https://camo.githubusercontent.com/7e33f5149e82b21d8a2207e6191694075361259e4a484c8f63e1a7abfdd8afb4/68747470733a2f2f692e70696e696d672e636f6d2f353634782f34372f62652f34392f34376265343963313664643735616164363464393631366530626333656532352e6a7067
2019 - A deep learning reconstruction framework for X-ray computed tomography with incomplete datahttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0224426
https://camo.githubusercontent.com/9e2f615619b5e288b30753eaf5114f8c11e2a7d809e99a4d24c32a065d5136bf/68747470733a2f2f692e70696e696d672e636f6d2f353634782f63362f66342f38302f63366634383066333431393834396231623935393863636333356462616563662e6a7067
2020 - Deep Learning Techniques for Inverse Problems in Imaginghttps://ieeexplore.ieee.org/abstract/document/9084378
2020 - [Review]: Deep learning for tomographic image reconstruction (closed)https://www.nature.com/articles/s42256-020-00273-z#author-information
https://camo.githubusercontent.com/85c21a84318f5a7aad482ea9668ec1fe51cf0291db04380aed17dbfa65f5df21/68747470733a2f2f6d656469612e737072696e6765726e61747572652e636f6d2f6d3638352f737072696e6765722d7374617469632f696d6167652f61727425334131302e313033382532467334323235362d3032302d30303237332d7a2f4d656469614f626a656374732f34323235365f323032305f3237335f466967315f48544d4c2e706e67
2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.https://www.nature.com/articles/s41551-019-0466-4
https://camo.githubusercontent.com/4cddfece3801edb3f0e996516a9be569155c5bc81efe6ae4c8d8239b2c426aaa/68747470733a2f2f692e70696e696d672e636f6d2f353634782f32352f33642f31622f32353364316262626161376366303132393166353032363266383437353937362e6a7067
2020 - End-To-End Convolutional Neural Network for 3D Reconstruction of Knee Bones from Bi-planar X-Ray Imageshttps://link.springer.com/chapter/10.1007%2F978-3-030-61598-7_12
https://camo.githubusercontent.com/48dcf18a44936345cfed1bd1103759ac628ad751ecd7ee8bba106e5340535632/68747470733a2f2f6d656469612e737072696e6765726e61747572652e636f6d2f6c773738352f737072696e6765722d7374617469632f696d6167652f63687025334131302e313030372532463937382d332d3033302d36313539382d375f31322f4d656469614f626a656374732f3530323032305f315f456e5f31325f466967325f48544d4c2e706e67
2020 - Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removalhttps://ieeexplore.ieee.org/document/9109572
https://camo.githubusercontent.com/ad246d3d699e5a7dd19da20e65155560f5e2a9b23e8aa0ec27e78f9ba6cb63be/68747470733a2f2f6965656578706c6f72652e696565652e6f72672f6d6564696173746f72655f6e65772f494545452f636f6e74656e742f6d656469612f34322f393234323334392f393130393537322f7965316162632d333030303334312d6c617267652e676966
2020 - Extreme Sparse X-ray Computed Laminography Via Convolutional Neural Networkshttps://ieeexplore.ieee.org/abstract/document/9288349/authors
https://camo.githubusercontent.com/ab98f6f9f203c3dfcb2189fc211c600120662abc63ace94d5f3a591853a1e83c/68747470733a2f2f6965656578706c6f72652e696565652e6f72672f6d6564696173746f72655f6e65772f494545452f636f6e74656e742f6d656469612f393238383136302f393238383136312f393238383334392f393238383334392d6669672d342d736f757263652d6c617267652e676966
2021 - [SliceGAN]: Generating 3D structures from a 2D slice with GAN-based dimensionality expansionhttps://arxiv.org/abs/2102.07708
https://camo.githubusercontent.com/4d09a220f0e3db6c88a7d0387b814f6dd024658efb2546f7264aa77a6447c292/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f34376539613636342d386234382d346331642d616461652d6364623035336435616263362f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313034323925324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130343239543038343035385a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d6232366433626234633338323438346438643133613264363634313164633735633035656633393935343766336332396463393134333638333761373636383126582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
2021 - DeepPhase: Learning phase contrast signal from dual energy X-ray absorption imageshttps://www.sciencedirect.com/science/article/abs/pii/S014193822100038X
https://camo.githubusercontent.com/d9739c8c41c54a038fb3e370ba0a8a2951de6024912148be808d3403e4d45f92/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f63663865643161632d623034302d346131302d393063342d6362663866383437363334392f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313036313525324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130363135543132313431315a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d3763353132643532333431653665336432356535326266366335613165333630656564633038663866353461306535663634383738656562626365636665666626582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
2022 - Machine learning denoising of high-resolution X-ray nano­tomography datahttps://journals.iucr.org/s/issues/2022/01/00/tv5025/index.html#BB12
https://camo.githubusercontent.com/32efe4362e623c73ea811a63f0e107e7ae192a9c0e2f3286785ce7b986414b74/68747470733a2f2f6a6f75726e616c732e697563722e6f72672f732f6973737565732f323032322f30312f30302f7476353032352f747635303235666967376d61672e6a7067
https://patch-diff.githubusercontent.com/axruff/DeepLearning#applications-image-registration
2014 - Do Convnets Learn Correspondence?https://arxiv.org/abs/1411.1091
https://camo.githubusercontent.com/c1d00924590c22470940f736845f406b5653065ad17f7dd752b75ef3b6b1d887/68747470733a2f2f692e70696e696d672e636f6d2f353634782f37332f66322f31322f37336632313266633035633837313531313132613833383166313930346363302e6a7067
2016 - Universal Correspondence Networkhttps://arxiv.org/abs/1606.03558
https://camo.githubusercontent.com/2dc56952a23e7aad2ee8d297c82f1e7e7fd0cea5eb2662dee844f370d4656d95/68747470733a2f2f6376676c2e7374616e666f72642e6564752f70726f6a656374732f75636e2f696d67732f6f766572766965772d6e6e5f736d2e706e67
2016 - Learning Dense Correspondence via 3D-guided Cycle Consistencyhttps://arxiv.org/abs/1604.05383
https://camo.githubusercontent.com/3cf0844cd3a39baf199e9f2a9d1e2cb6eb3bc52b974e6879a07d6b6f0e9e3db7/68747470733a2f2f70656f706c652e656563732e6265726b656c65792e6564752f7e74696e676875697a2f70726f6a656374732f6c6561726e4379636c652f696d616765732f7465617365722e706e67
2017 - Convolutional neural network architecture for geometric matchinghttps://arxiv.org/abs/1703.05593
[github]https://github.com/ignacio-rocco/cnngeometric_pytorch
https://camo.githubusercontent.com/48fd619858efbd3abcb405af58a1fa6632d01892c06cb73c262980ad81f35f24/68747470733a2f2f7777772e64692e656e732e66722f77696c6c6f772f72657365617263682f636e6e67656f6d65747269632f696d616765732f6469616772616d2e706e67
2018 - [DGC-Net]: Dense Geometric Correspondence Networkhttps://arxiv.org/abs/1810.08393
[github]https://github.com/AaltoVision/DGC-Net
https://camo.githubusercontent.com/f140e6285fcebc6848a9ef93b2f9de6eff84102b4381b85f4fc8359a4227b1a5/68747470733a2f2f692e70696e696d672e636f6d2f353634782f35332f37362f64662f35333736646636346133656633353761373330366132643866393661633430372e6a7067
2018 - An Unsupervised Learning Model for Deformable Medical Image Registrationhttps://arxiv.org/abs/1802.02604
https://camo.githubusercontent.com/5851002225d77aff156b0dfd4558e0fa9638c38f744e0b06534238275be25f1a/68747470733a2f2f766974616c61622e6769746875622e696f2f61727469636c652f696d616765732f756e737570657276697365642d726567697374726174696f6e2f666967757265322e706e67
2018 - VoxelMorph: A Learning Framework for Deformable Medical Image Registrationhttps://arxiv.org/abs/1809.05231
[github]https://github.com/voxelmorph/voxelmorph
https://camo.githubusercontent.com/bc90cb8a7307d2dc11431c6fcaff48b50ef272f895445d7bfb0fcebad79821cf/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f67726f756e6461692d7765622d70726f642f6d656469612532467573657273253246757365725f313425324670726f6a6563745f333838323936253246696d6167657325324678322e706e67
2019 - A Deep Learning Framework for Unsupervised Affine and Deformable Image Registrationhttps://arxiv.org/abs/1809.06130
https://camo.githubusercontent.com/fdf27b952ff8cbf340a14e0d066d143c60a82d9064b5b31b11d8323a575a5337/68747470733a2f2f6172732e656c732d63646e2e636f6d2f636f6e74656e742f696d6167652f312d73322e302d53313336313834313531383330303439352d677231322e6a7067
2019 - Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaceshttps://arxiv.org/abs/1903.03545
https://patch-diff.githubusercontent.com/axruff/DeepLearning/blob/master
2020 - RANSAC-Flow: generic two-stage image alignmenthttps://arxiv.org/abs/2004.01526
https://camo.githubusercontent.com/67dd96e26ab83c689a5eae054bacc56ac6c2bf775b8d9e01d9ca779ec03f6559/687474703a2f2f696d6167696e652e656e70632e66722f7e7368656e782f52414e5341432d466c6f772f696d672f6f766572766965772e6a7067
https://patch-diff.githubusercontent.com/axruff/DeepLearning#applications-video
https://arxiv.org/abs/1703.10025https://arxiv.org/abs/1703.10025
https://arxiv.org/abs/1611.07715https://arxiv.org/abs/1611.07715
Video-to-Video Synthesis (2018)https://arxiv.org/abs/1808.06601
[github]https://github.com/NVIDIA/vid2vid
2017 - PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volumehttps://arxiv.org/abs/1709.02371
[github]https://github.com/axruff/pytorch-pwc
https://camo.githubusercontent.com/e4df7144c148743620a03d39bc3208380ba01244228a9e540d3c77e721f43512/68747470733a2f2f72657365617263682e6e76696469612e636f6d2f73697465732f64656661756c742f66696c65732f7075626c69636174696f6e732f7077636e65745f302e706e67
2020 - Softmax Splatting for Video Frame Interpolationhttps://arxiv.org/abs/2003.05534
[github]https://github.com/sniklaus/softmax-splatting
https://camo.githubusercontent.com/a2d224866d15af56a008ccd3461f476e8337a5cff5f8f54e2581d6f49e558858/68747470733a2f2f7062732e7477696d672e636f6d2f6d656469612f4553394e52724155384141525350412e6a7067
2017 - [TOFlow] Video Enhancement with Task-Oriented Flowhttps://arxiv.org/abs/1711.09078
https://camo.githubusercontent.com/1e7164e0045643f45d822d6e1bdb2ffd4d01ee205b2ee0f26f0bd57b434fdb53/687474703a2f2f746f666c6f772e637361696c2e6d69742e6564752f66696c65732f7465617365722e6a7067
https://patch-diff.githubusercontent.com/axruff/DeepLearning#applications-simulations
2020 - Automating turbulence modelling by multi-agent reinforcement learninghttps://www.nature.com/articles/s42256-020-00272-0
https://camo.githubusercontent.com/0de9a2934123c721e69ca894082927474c75e4d73469fb6c0bdfaeb0ae90b133/68747470733a2f2f736378312e622d63646e2e6e65742f63737a2f6e6577732f383030612f323032312f322d7265736561726368657273632e6a7067
https://patch-diff.githubusercontent.com/axruff/DeepLearning#application-denoising-and-superresolution
2017 - "Zero-Shot" Super-Resolution using Deep Internal Learninghttps://arxiv.org/abs/1712.06087
https://camo.githubusercontent.com/9c835ae3d8efecafcede62c6eec985a2204a122ef93e102fa916c5060a9ed440/68747470733a2f2f692e70696e696d672e636f6d2f353634782f35632f38302f66632f35633830666362663938626164396330633961613861626230663134323732342e6a7067
2018 - Residual Dense Network for Image Restorationhttps://arxiv.org/abs/1812.10477v1
[github]https://github.com/yulunzhang/RDN
https://camo.githubusercontent.com/c7c65dfe79eb18d70d5c1dc944d8a7118ef12df184b7971f36518570ecacd95d/68747470733a2f2f692e70696e696d672e636f6d2f353634782f31322f37652f62342f31323765623464666266343832646231626134333665613936303832316661652e6a7067
2018 - Image Super-Resolution Using Very Deep Residual Channel Attention Networkshttps://arxiv.org/abs/1807.02758
https://camo.githubusercontent.com/cdc2593682d264f0419e594205daa446bcb2502e189ef5259d2c546dfd7fb227/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f313230302f312a704f5546554e674251775368334555624a49326d33512e706e67
2019 - Noise2Self: Blind Denoising by Self-Supervisionhttps://www.semanticscholar.org/paper/Noise2Self%3A-Blind-Denoising-by-Self-Supervision-Batson-Royer/ea9cf47573638745c9992cf9c5ebdabadd3c6849
https://camo.githubusercontent.com/223fc431b928d8f358d00521f39c2c73063ace8aa273889f4fef8fb1177b1915/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f37626130633065662d333433372d343266362d393063372d3334636562666562306132642f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313036333025324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130363330543133323034335a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d6232353563323736663337663230396663656234343733383436646565353639316331633165356137656539333837393362623130323435663966623935613026582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
2020 - Improving Blind Spot Denoising for Microscopyhttps://arxiv.org/abs/2008.08414
https://camo.githubusercontent.com/c1db9e9969d2f6345d7ba0d3f14522dccee9773e3e0dc6898ca5defda96c2d01/68747470733a2f2f692e70696e696d672e636f6d2f353634782f31312f39642f38662f31313964386662376232613064643861653330643035303630363165373736622e6a7067
2021 - Denoising-based Image Compression for Connectomicshttps://www.biorxiv.org/content/10.1101/2021.05.29.445828v1
https://camo.githubusercontent.com/89da4db05e68e8b281ac345c727fd15e30b52242e602b4ce05d21d7e24ba0cae/68747470733a2f2f692e70696e696d672e636f6d2f353634782f34642f38392f65312f34643839653130393332373138363636613837386530316133346635303836632e6a7067
2021 - Task-Assisted GAN for Resolution Enhancement and Modality Translation in Fluorescence Microscopyhttps://patch-diff.githubusercontent.com/axruff/DeepLearning/blob/master
https://camo.githubusercontent.com/a9aee2a267fa37282e89b284ea41dadc04f01e7ece40fa373782b245d36e0e5f/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f35633738366134392d306231612d346139342d383761612d3333323666343834353564312f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313037323125324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130373231543132303335385a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d6431646364356632373732396563316531303833633566393263396565653337623664646565623434326565363335376566386233346661393230373436613626582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
https://patch-diff.githubusercontent.com/axruff/DeepLearning#applications-inpainting
2018 - Image Inpainting for Irregular Holes Using Partial Convolutionshttp://openaccess.thecvf.com/content_ECCV_2018/papers/Guilin_Liu_Image_Inpainting_for_ECCV_2018_paper.pdf
[github official]https://github.com/NVIDIA/partialconv
[github]https://github.com/MathiasGruber/PConv-Keras
https://camo.githubusercontent.com/e9bea3133877a6513508121cc0b3f19a98c0b7c68757b650379456a20bbde956/68747470733a2f2f692e70696e696d672e636f6d2f353634782f36332f66612f61332f36336661613333386562613235323235633765383466316433626164373464332e6a7067
2017 - Globally and Locally Consistent Image Completionhttp://iizuka.cs.tsukuba.ac.jp/projects/completion/data/completion_sig2017.pdf
[github]https://github.com/satoshiiizuka/siggraph2017_inpainting
https://camo.githubusercontent.com/ab7e5335d862a995d30b6bc23f1af640c801ecd1334ec988bd2aa94c9d5201aa/687474703a2f2f69697a756b612e63732e7473756b7562612e61632e6a702f70726f6a656374732f636f6d706c6574696f6e2f696d616765732f7465617365722f666c69636b725f345f6f2e706e67
2017 - Generative Image Inpainting with Contextual Attentionhttps://arxiv.org/abs/1801.07892
[github]https://github.com/JiahuiYu/generative_inpainting
https://user-images.githubusercontent.com/22609465/35364552-6e9dfab0-0135-11e8-8bc1-5f370a9f4b0a.png
2018 - Free-Form Image Inpainting with Gated Convolutionhttps://arxiv.org/abs/1806.03589
https://user-images.githubusercontent.com/22609465/41198673-1aac4f2e-6c38-11e8-9f75-6bac82b94265.jpg
https://patch-diff.githubusercontent.com/axruff/DeepLearning#applications-photography
Photo-realistic single image super-resolution using a generative adversarial network (2016)https://arxiv.org/abs/1609.04802
[github]https://github.com/tensorlayer/srgan
https://camo.githubusercontent.com/98860a0240445d7877d5a1e913ee6324efe637416435a757efd8baa1315dbf07/68747470733a2f2f766974616c61622e6769746875622e696f2f646565702d6c6561726e696e672f696d616765732f737267616e2d73757065722d7265736f6c7574696f6e2f666967757265322e706e67
A Closed-form Solution to Photorealistic Image Stylization (2018)https://arxiv.org/abs/1802.06474
[github]https://github.com/NVIDIA/FastPhotoStyle
https://camo.githubusercontent.com/2080a78bc0643700cebe3f434f53930b2f96202ffd88184e1faf1e1314fe2d5f/687474703a2f2f692e677a6e2e6a702f696d672f323031382f30322f32312f6e76696469612d6661737470686f746f7374796c652f30302e6a7067
2021 - COIN: COmpression with Implicit Neural representationshttps://www.semanticscholar.org/paper/COIN%3A-COmpression-with-Implicit-Neural-Dupont-Goli'nski/1bf444b861acc3dad72d968c2c69bcb863885ff9
https://camo.githubusercontent.com/df236b0eae4ed927875dd0ed80bc7b8649fb23e078f0eee0098f03e9d39492f1/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f37356563626662632d366563642d346462302d393838352d3831633065333333653461652f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313036323225324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130363232543131323333355a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d3139313065326132633835653665623033323666626331393338616564316135373438653738376632373361373663356233393738623231666639323330326126582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
https://patch-diff.githubusercontent.com/axruff/DeepLearning#applications-misc
[pix2code]: Generating Code from a Graphical User Interface Screenshothttps://arxiv.org/abs/1705.07962v2
[github]https://github.com/tonybeltramelli/pix2code
https://camo.githubusercontent.com/277db401a3ba33866823e9bcb4396747c54697b5734704dd67510832cf19419f/68747470733a2f2f692e70696e696d672e636f6d2f353634782f62652f65362f33302f62656536333032616563316338306438316261306432303662343732323262392e6a7067
Fast Interactive Object Annotation with Curve-GCN (2019)https://arxiv.org/abs/1903.06874v1
https://raw.githubusercontent.com/fidler-lab/curve-gcn/master/docs/model.png
2017 - Learning Fashion Compatibility with Bidirectional LSTMshttps://arxiv.org/abs/1707.05691
[github]https://github.com/xthan/polyvore
https://camo.githubusercontent.com/3100942ece4e92f462607dec113eb9eb5a756493459a490e2771f943010f8d8d/68747470733a2f2f692e70696e696d672e636f6d2f353634782f34622f61662f66632f34626166666335316363383762313335346564396538386363386264353334652e6a7067
2020 - A Systematic Literature Review on the Use of Deep Learning in Software Engineering Researchhttps://arxiv.org/abs/2009.06520
https://camo.githubusercontent.com/a2faea3eaffef8e44e4496f544b5f439535944b9481ccfbd9b0e13fbd21d554c/68747470733a2f2f7062732e7477696d672e636f6d2f6d656469612f45693258472d455830414132787a443f666f726d61743d6a7067266e616d653d6c61726765
2020 - Fourier Neural Operator for Parametric Partial Differential Equationshttps://arxiv.org/abs/2010.08895
https://patch-diff.githubusercontent.com/axruff/DeepLearning#software
Caffe: Convolutional Architecture for Fast Feature Embeddinghttps://arxiv.org/abs/1408.5093
Tune: A Research Platform for Distributed Model Selection and Training (2018)https://arxiv.org/abs/1807.05118
[github]https://github.com/ray-project/ray/tree/master/python/ray/tune
Glow: Compiler for Neural Network hardware acceleratorshttps://github.com/pytorch/glow
https://github.com/pytorch/glow/raw/master/docs/3LevelIR.png
Lucid: A collection of infrastructure and tools for research in neural network interpretabilityhttps://github.com/tensorflow/lucid
PySyft: A generic framework for privacy preserving deep learninghttps://arxiv.org/abs/1811.04017
[github]https://github.com/OpenMined/PySyft
Crypten: A framework for Privacy Preserving Machine Learninghttps://crypten.ai/
[github]https://github.com/facebookresearch/crypten
[Snorkel]: Programmatically Building and Managing Training Datahttps://www.snorkel.org/
[Netron ] Visualizer for deep learning and machine learning modelshttps://github.com/lutzroeder/Netron
https://raw.githubusercontent.com/lutzroeder/netron/master/media/screenshot.png
[Interactive Tools] for ML, DL and Mathhttps://github.com/Machine-Learning-Tokyo/Interactive_Tools
[Efemarai]https://efemarai.com/
[mlflow] - An open source platform for the machine learning lifecyclehttps://mlflow.org/
OpenAI Microscopehttps://microscope.openai.com/models
https://camo.githubusercontent.com/bf28f9a725ca8cd806c1d1c6581037d0fbe102ac71ea5cc5edd4ed0d8758dd5f/68747470733a2f2f692e70696e696d672e636f6d2f353634782f38362f30632f37352f38363063373565633837356538356338643830613530646365333334656261622e6a7067
[TorchIO] - Medical image preprocessing and augmentation toolkit for deep learninghttps://github.com/fepegar/torchio
https://raw.githubusercontent.com/fepegar/torchio/master/docs/images/gifs_readme/1_Lambda_mri.png
[Ignite] - high-level library to help with training and evaluating neural networkshttps://github.com/pytorch/ignite
https://github.com/pytorch/ignite/raw/master/assets/logo/ignite_logo_mixed.svg
[Cadene] - Pretrained models for Pytorchhttps://github.com/Cadene/pretrained-models.pytorch
[PyTorch Toolbelt] - a Python library with a set of bells and whistles for PyTorch for fast R&D prototypinghttps://github.com/BloodAxe/pytorch-toolbelt
[PyTorch Lightning]- The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplatehttps://github.com/PyTorchLightning/pytorch-lightning
https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_images/logos/lightning_logo-name.png
[Rapid] - Open GPU Data Sciencehttps://rapids.ai/index.html
https://camo.githubusercontent.com/6b3c3bec1a25aa1f329ad063a2fb05a5c156298cf348a26e62abcb5624082c01/68747470733a2f2f692e70696e696d672e636f6d2f353634782f30622f31642f37312f30623164373162343962373932393734393838613534393232626535313132302e6a7067
[DALI] - NVIDIA Data Loading Libraryhttps://developer.nvidia.com/dali
https://camo.githubusercontent.com/c4cc6fb6196af859fb0c13b582cf9686c155b136c132678966c376c5b18e52cf/68747470733a2f2f646576656c6f7065722e6e76696469612e636f6d2f73697465732f64656661756c742f66696c65732f616b616d61692f64616c692e706e67
[Ray] - Fast and Simple Distributed Computinghttps://ray.io/
[PhotonAI] - A high level Python API for designing and optimizing machine learning pipelines.https://www.photon-ai.com/
https://camo.githubusercontent.com/3dd7188311b02f03442640c40fdf39a3c31f3bdca4fe3da2e11fcf3270b621f8/68747470733a2f2f73332e75732d776573742d322e616d617a6f6e6177732e636f6d2f7365637572652e6e6f74696f6e2d7374617469632e636f6d2f34366337323436302d623065652d346335312d613937372d3561353065356137326461312f556e7469746c65642e706e673f582d416d7a2d416c676f726974686d3d415753342d484d41432d53484132353626582d416d7a2d43726564656e7469616c3d414b49415437334c324734354f334b5335325935253246323032313034313425324675732d776573742d322532467333253246617773345f7265717565737426582d416d7a2d446174653d3230323130343134543133323335375a26582d416d7a2d457870697265733d383634303026582d416d7a2d5369676e61747572653d3335363635376532623766323661626563386163363464623330633563333334343430663633336530323363346237663064613436306131393461663732363726582d416d7a2d5369676e6564486561646572733d686f737426726573706f6e73652d636f6e74656e742d646973706f736974696f6e3d66696c656e616d65253230253344253232556e7469746c65642e706e67253232
[DeepImageJ]: A user-friendly environment to run deep learning models in ImageJhttps://deepimagej.github.io/deepimagej/
https://camo.githubusercontent.com/01a9618ef19671414b8b75c0ebb6faa8d416847533202505951fddeaaa3f0eea/68747470733a2f2f64656570696d6167656a2e6769746875622e696f2f64656570696d6167656a2f696d616765732f64656570696d6167656a5f6c6f676f2e706e67
[ImJoy]: Deep Learning Made Easy!https://imjoy.io/#/
https://camo.githubusercontent.com/22e4e76a45b416bcd82a3934f8ce568c66461e366f5ab76d4e3aee12f8fd9385/68747470733a2f2f6d656469612e737072696e6765726e61747572652e636f6d2f66756c6c2f737072696e6765722d7374617469632f696d6167652f61727425334131302e313033382532467334313539322d3031392d303632372d302f4d656469614f626a656374732f34313539325f323031395f3632375f466967315f48544d4c2e706e673f61733d77656270
[BioImage.IO]: Bioimage Model Zoohttps://bioimage.io/#/
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[DeepImageTranslator]: a free, user-friendly graphical interface for image translation using deep-learning and its applications in 3D CT image analysishttps://prelights.biologists.com/highlights/deepimagetranslator-a-free-user-friendly-graphical-interface-for-image-translation-using-deep-learning-and-its-applications-in-3d-ct-image-analysis/
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Analytics Zoo (Intel): Distributed TensorFlow, PyTorch, Keras and BigDL on Apache Spark & Rayhttps://github.com/intel-analytics/analytics-zoo
https://github.com/intel-analytics/analytics-zoo/raw/master/docs/docs/Image/logo.jpg
[OpenMMLab] - Open source projects for academic research and industrial applicationshttps://openmmlab.com/home
https://patch-diff.githubusercontent.com/axruff/DeepLearning/blob/master
2021 - BTrack: Bayesian Trackerhttps://github.com/quantumjot/BayesianTracker
https://raw.githubusercontent.com/quantumjot/arboretum/master/examples/napari.png
https://patch-diff.githubusercontent.com/axruff/DeepLearning#overview
2016 - An Analysis of Deep Neural Network Models for Practical Applicationshttps://arxiv.org/abs/1605.07678
2017 - Revisiting Unreasonable Effectiveness of Data in Deep Learning Erahttps://arxiv.org/abs/1707.02968
2019 - High-performance medicine: the convergence of human and artificial intelligencehttps://www.nature.com/articles/s41591-018-0300-7
2020 - Maithra Raghu, Eric Schmidt. A Survey of Deep Learning for Scientific Discoveryhttps://arxiv.org/abs/2003.11755v1
[ml-surveys [github]] - a selection of survey papers summarizing the advances in the fieldhttps://github.com/eugeneyan/ml-surveys
[DALI] - NVIDIA Data Loading Library https://developer.nvidia.com/dali
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2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scanshttps://www.nature.com/articles/s42256-021-00307-0
https://patch-diff.githubusercontent.com/axruff/DeepLearning#opinions
2016 - Building Machines That Learn and Think Like Peoplehttps://www.semanticscholar.org/paper/Building-Machines-That-Learn-and-Think-Like-People-Lake-Ullman/5721a0c623aeb12a65b4d6f5a5c83a5f82988d7c
2016 - A Berkeley View of Systems Challenges for AIhttps://arxiv.org/abs/1712.05855
2018 - Deep Learning: A Critical Appraisalhttps://arxiv.org/abs/1801.00631
2018 - Human-level intelligence or animal-like abilities?https://dl.acm.org/citation.cfm?id=3271625
2018 - When Will AI Exceed Human Performance? Evidence from AI Expertshttps://arxiv.org/abs/1705.08807
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2018 - The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigationhttps://docs.google.com/document/d/e/2PACX-1vQzbSybtXtYzORLqGhdRYXUqiFsaEOvftMSnhVgJ-jRh6plwkzzJXoQ-sKtej3HW_0pzWTFY7-1eoGf/pub
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2018 - Deciphering China’s AI Dream: The context, components, capabilities, and consequences of China’s strategy to lead the world in AIhttps://www.fhi.ox.ac.uk/deciphering-chinas-ai-dream/
2018 - The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communitieshttps://arxiv.org/abs/1803.03453
2019 - Deep Nets: What have they ever done for Vision?https://arxiv.org/abs/1805.04025
2020 - State of AI Report 2020https://www.stateof.ai/
2020 - The role of artificial intelligence in achieving the Sustainable Development Goalshttps://www.nature.com/articles/s41467-019-14108-y
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2020 - The Next Decade in AI: Four Steps Towards Robust Artificial Intelligencehttps://arxiv.org/abs/2002.06177
2021 - Why AI is Harder Than We Think by Melanie Mitchellhttps://arxiv.org/abs/2104.12871
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