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Title: GitHub - axruff/TransferLearning: A collection of papers, datasets and software on Transfer Learning and Domain Adaptation

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Description: A collection of papers, datasets and software on Transfer Learning and Domain Adaptation - axruff/TransferLearning

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	https://patch-diff.githubusercontent.com/axruff/TransferLearning#surveys
A Survey on Transfer Learning (2010)	https://www.cse.ust.hk/~qyang/Docs/2009/tkde_transfer_learning.pdf
Transfer learning for visual categorization: A survey (2015)	https://ieeexplore.ieee.org/document/6847217
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Domain Adaptation for Visual Applications: A Comprehensive Survey (2017)	https://arxiv.org/abs/1702.05374
Visual domain adaptation: A survey of recent advances (2015)	https://ieeexplore.ieee.org/document/7078994
	https://camo.githubusercontent.com/4a0e11b6a28fa78a4d91bfbffdb418ebe5ba62f98a3f6ca9f3cfd18b5da672c7/68747470733a2f2f706c616365686f6c642e69742f31352f6335666631352f3030303030303f746578743d2b
Deep Visual Domain Adaptation: A Survey (2018)	https://arxiv.org/abs/1802.03601
A survey on heterogeneous transfer learning (2017)	https://journalofbigdata.springeropen.com/articles/10.1186/s40537-017-0089-0
Transfer learning for cross-dataset recognition: A survey (2017)	https://arxiv.org/abs/1705.04396
http://ai.bu.edu/visda-2017/	http://ai.bu.edu/visda-2017/
	https://patch-diff.githubusercontent.com/axruff/TransferLearning#unsorted
2018 - Do Better ImageNet Models Transfer Better?	https://arxiv.org/abs/1805.08974
	https://camo.githubusercontent.com/84a24713925f89e42fe32ea53c5f4dc1baa1bf2a0b2c8ca6b9d95630bb78aa7b/68747470733a2f2f6d656469612e61727869762d76616e6974792e636f6d2f72656e6465722d6f75747075742f323234393835392f74736e655f6669677572652e706e67
2019 - Revisiting Self-Supervised Visual Representation Learning	https://arxiv.org/abs/1901.09005
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2019 - A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark	https://arxiv.org/abs/1910.04867
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2013 - Simulation as an engine of physical scene understanding	https://www.pnas.org/content/110/45/18327
2016 - Playing for Data: Ground Truth from Computer Games	https://arxiv.org/abs/1608.02192
	https://github.com/axruff/ML_papers/raw/master/images/PlayingforData.png
2016 - RenderGAN: Generating Realistic Labeled Data	https://arxiv.org/abs/1611.01331
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2017 - On Pre-Trained Image Features and Synthetic Images for Deep Learning)	https://arxiv.org/abs/1710.10710
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2017 - Keep it Unreal: Bridging the Realism Gap for 2.5D Recognition with Geometry Priors Only	https://arxiv.org/abs/1804.09113
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2018 - Seeing Beyond Appearance - Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition	https://arxiv.org/abs/1810.04158
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2019 - Implicit 3D Orientation Learning for 6D Object Detection from RGB Images	https://arxiv.org/abs/1902.01275
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2018 - Learning to Segment via Cut-and-Paste	http://openaccess.thecvf.com/content_ECCV_2018/papers/Tal_Remez_Learning_to_Segment_ECCV_2018_paper.pdf
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2021 - Auto-Tuned Sim-to-Real Transfer	https://arxiv.org/abs/2104.07662
[github]	https://yuqingd.github.io/autotuned-sim2real/
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Fine-grained recognition in the wild: A multi-task domain adaptation approach (2017)	https://arxiv.org/abs/1709.02476
Deep transfer metric learning (2015)	https://ieeexplore.ieee.org/document/7298629
Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation (2017)	https://arxiv.org/abs/1705.00609
[98]	https://arxiv.org/abs/1702.08400
Asymmetric tri-training for unsupervised domain adaptation (2017)	https://arxiv.org/abs/1702.08400
[DTN] Deep transfer network: Unsupervised domain adaptation (2015)	https://arxiv.org/abs/1503.00591
	https://patch-diff.githubusercontent.com/axruff/TransferLearning#statistic-criterion
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[DDC] Deep domain confusion: Maximizing for domain invariance (2014)	https://arxiv.org/abs/1412.3474
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[73]	https://arxiv.org/abs/1502.02791
[DAN] Learning transferable features with deep adaptation networks (2015)	https://arxiv.org/abs/1502.02791
[JAN] Deep transfer learning with joint adaptation networks (2016)	https://arxiv.org/abs/1605.06636
[RTN] Unsupervised domain adaptation with residual transfer networks (2016)	https://arxiv.org/abs/1602.04433
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Associative Domain Adaptation (2017)	https://arxiv.org/abs/1708.00938
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Return of frustratingly easy domain adaptation (2015)	https://arxiv.org/abs/1511.05547
	https://patch-diff.githubusercontent.com/axruff/TransferLearning#architectural-criterion
Deeper, broader and artier domain generalization (2017)	https://arxiv.org/abs/1710.03077
	https://camo.githubusercontent.com/b026754f27dc4ec9a55449b4b2da1ed859bd71ee903e5c92e2964c42aeaa8075/687474703a2f2f7777772e656563732e716d756c2e61632e756b2f7e646c3330372f696d672f70726f6a6563745f696d67312e706e67
	https://patch-diff.githubusercontent.com/axruff/TransferLearning#geometric-criterion
[Dlid]: Deep learning for domain adaptation by interpolating between domains (2013)	http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.664.4509
	https://patch-diff.githubusercontent.com/axruff/TransferLearning#adversarial-based-approaches
	https://patch-diff.githubusercontent.com/axruff/TransferLearning#generative-models
2014 - Generative Adversarial Networks	https://arxiv.org/abs/1406.2661
	https://camo.githubusercontent.com/583ab1044327ad645fec38b373832f325df8a7a2d07e8521f08de21b2b11a76f/68747470733a2f2f706c616365686f6c642e69742f31352f6666303030302f3030303030303f746578743d2b
2015 - [DANN] Domain-Adversarial Training of Neural Networks	https://arxiv.org/abs/1505.07818
[github]	https://github.com/fungtion/DANN
	https://camo.githubusercontent.com/5201a6af692fe44c22cc2dfda8e9db02fb0e0ffc/68747470733a2f2f73312e617831782e636f6d2f323031382f30312f31322f70384b5479442e6d642e6a7067
2015 - [LapGAN] Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks	https://arxiv.org/abs/1506.05751
	https://camo.githubusercontent.com/7d1a9a2c2703dfe6566acd5afcda337f472f4918c18dee6ba2a1b0a3514aa490/687474703a2f2f736f756d6974682e63682f65796573637265616d2f696d616765732f4c415047414e2e706e67
2016 - Improved techniques for training GANs	https://arxiv.org/abs/1606.03498
github	https://github.com/openai/improved-gan
2016 - Domain Separation Networks	https://arxiv.org/abs/1608.06019
	https://camo.githubusercontent.com/54c4bda6c4febff7a92668bc6c278be234b0c27b168d6ef2b91f9e1aaef4c18d/68747470733a2f2f692e70696e696d672e636f6d2f353634782f64652f35302f66612f64653530666163383130373465313663613738313134663738613337393234362e6a7067
2016 - [PixelDA] Unsupervised pixel-level domain adaptation with generative adversarial networks	https://arxiv.org/abs/1612.05424
	https://camo.githubusercontent.com/4a0e11b6a28fa78a4d91bfbffdb418ebe5ba62f98a3f6ca9f3cfd18b5da672c7/68747470733a2f2f706c616365686f6c642e69742f31352f6335666631352f3030303030303f746578743d2b
	https://camo.githubusercontent.com/6328c32ad3205b52be4f1b137397ec651645c65c26c1c0b788ad9720843b20e4/68747470733a2f2f692e70696e696d672e636f6d2f353634782f66382f35322f31652f66383532316534353431353736323436356535653031343532613936336133312e6a7067
2016 - [CoGAN] Coupled generative adversarial networks	https://arxiv.org/abs/1606.07536
2016 - Pixel-level domain transfer	https://arxiv.org/pdf/1603.07442.pdf
[github]	https://github.com/fxia22/PixelDTGAN
	https://camo.githubusercontent.com/7ccd19520bebcdae8b42a4f1c622ee7c59feb0d26a1f468b93e0a08bbd22dada/68747470733a2f2f7062732e7477696d672e636f6d2f6d656469612f43674b6851326857454141453233312e6a70673a6c61726765
104	https://patch-diff.githubusercontent.com/axruff/TransferLearning/blob/master
2016 - Learning from Simulated and Unsupervised Images through Adversarial Training	https://arxiv.org/abs/1612.07828
	https://github.com/axruff/ML_papers/raw/master/images/123.png
2017 - [MAD-GAN] Multi-Agent Diverse Generative Adversarial Networks	https://arxiv.org/abs/1704.02906
2017 - [ADDA] Adversarial discriminative domain adaptation	https://arxiv.org/abs/1702.05464
	https://raw.githubusercontent.com/joshua19881228/my_blogs/master/Computer_Vision/Reading_Note/figures/ADDA_1.jpg
2017 - [PacGAN] PacGAN: The power of two samples in generative adversarial networks	https://arxiv.org/abs/1712.04086
2017 - Wasserstein GAN	https://arxiv.org/abs/1701.07875
	https://camo.githubusercontent.com/583ab1044327ad645fec38b373832f325df8a7a2d07e8521f08de21b2b11a76f/68747470733a2f2f706c616365686f6c642e69742f31352f6666303030302f3030303030303f746578743d2b
2017 - [PGAN]: Progressive Growing of GANs for Improved Quality, Stability, and Variation	https://arxiv.org/abs/1710.10196v3
	https://camo.githubusercontent.com/4a0e11b6a28fa78a4d91bfbffdb418ebe5ba62f98a3f6ca9f3cfd18b5da672c7/68747470733a2f2f706c616365686f6c642e69742f31352f6335666631352f3030303030303f746578743d2b
	https://camo.githubusercontent.com/eb93076d21d3c666f65170d47353a1c533839e0cf70249f656fe7ba1f4af80f9/68747470733a2f2f61647269616e636f6c7965722e66696c65732e776f726470726573732e636f6d2f323031382f30352f70726f67726573736976652d67616e732d6669672d312e6a7065673f773d363430
2017 - Improved Adversarial Systems for 3D Object Generation and Reconstruction	https://arxiv.org/abs/1707.09557
2018 - Toward Multimodal Image-to-Image Translation	https://arxiv.org/abs/1711.11586
	https://camo.githubusercontent.com/6a21505b95c92371467360aa85b799ef7442dd9d1e23d554f15f145a85d1d3c1/68747470733a2f2f6a756e79616e7a2e6769746875622e696f2f42696379636c6547414e2f696e6465785f66696c65732f7465617365722e6a7067
2018 - From Source to Target and Back: Symmetric Bi-Directional Adaptive GAN	http://openaccess.thecvf.com/content_cvpr_2018/html/Russo_From_Source_to_CVPR_2018_paper.html
2018 - SRDA: Generating Instance Segmentation Annotation Via Scanning, Reasoning And Domain Adaptation	https://arxiv.org/abs/1801.08839
	https://camo.githubusercontent.com/583ab1044327ad645fec38b373832f325df8a7a2d07e8521f08de21b2b11a76f/68747470733a2f2f706c616365686f6c642e69742f31352f6666303030302f3030303030303f746578743d2b
2018 - How good is my GAN?	https://arxiv.org/abs/1807.09499
	https://camo.githubusercontent.com/1e86033f0b8f1ba904f377e4cb7951ae46c503fbd187ed526d705d04ef7b5390/687474703a2f2f74686f74682e696e7269616c7065732e66722f72657365617263682f67616e6576616c2f696d616765732f666967312e706e67
2018 - A Style-Based Generator Architecture for Generative Adversarial Networks	https://arxiv.org/abs/1812.04948
	https://camo.githubusercontent.com/583ab1044327ad645fec38b373832f325df8a7a2d07e8521f08de21b2b11a76f/68747470733a2f2f706c616365686f6c642e69742f31352f6666303030302f3030303030303f746578743d2b
	https://camo.githubusercontent.com/cc4708cafd34693aa300200813b1e5550f036c0b5f2cd2717438014054f8d0eb/68747470733a2f2f72657365617263682e6e76696469612e636f6d2f73697465732f64656661756c742f66696c65732f7075626c69636174696f6e732f7374796c6567616e2d7465617365722d736d616c6c2e6a7067
2019 - U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation	https://arxiv.org/abs/1907.10830
	https://camo.githubusercontent.com/583ab1044327ad645fec38b373832f325df8a7a2d07e8521f08de21b2b11a76f/68747470733a2f2f706c616365686f6c642e69742f31352f6666303030302f3030303030303f746578743d2b
	https://camo.githubusercontent.com/ad6bc7dfe423705013c98494327bf0f285fc6528a292447b5c2f9338a8ba09e7/68747470733a2f2f707974686f6e617765736f6d652e636f6d2f636f6e74656e742f696d616765732f323031392f30382f67656e657261746f722e706e67
2020 - [BlockGAN]: Learning 3D Object-aware Scene Representations from Unlabelled Images	https://arxiv.org/abs/2002.08988
	https://camo.githubusercontent.com/cf2917af4b1d375933bab180382f0b2d633a24b38946863fdb879df0b0baa394/68747470733a2f2f692e70696e696d672e636f6d2f353634782f38662f31302f62332f38663130623330393936366139666166353433303939366161376431313534352e6a7067
2021 - DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort	https://www.semanticscholar.org/paper/DatasetGAN%3A-Efficient-Labeled-Data-Factory-with-Zhang-Ling/786cb74290340443e9d02ffd9669f5e2a18878b5
	https://camo.githubusercontent.com/ba5dd897b7c56d47708af0e0371026e25922bbbe3cb33c819fdd6ea2383f1973/68747470733a2f2f692e70696e696d672e636f6d2f353634782f66342f30392f62392f66343039623934333830346661383732353766626431663361616432393437662e6a7067
2021 - DriveGAN: Towards a Controllable High-Quality Neural Simulation	https://www.semanticscholar.org/paper/DriveGAN%3A-Towards-a-Controllable-High-Quality-Kim-Philion/02f316857c1d20649b18e5fec3e92dda8ef1d0a0
	https://camo.githubusercontent.com/bf7ae8222f06cfdfee18e5d92bb170d4001c8f9c17951faa89ff33255fe38e35/68747470733a2f2f692e70696e696d672e636f6d2f353634782f33372f37622f65342f33373762653432633333336431323930636539613834643965363830323064632e6a7067
	https://patch-diff.githubusercontent.com/axruff/TransferLearning#non-generative-models
	https://patch-diff.githubusercontent.com/axruff/TransferLearning#reconstruction-based-approaches
	https://patch-diff.githubusercontent.com/axruff/TransferLearning#others
[link]	https://github.com/hindupuravinash/the-gan-zoo
	https://github.com/hindupuravinash/the-gan-zoo/raw/master/The_GAN_Zoo.jpg
[link]	https://arxiv.org/abs/1706.05208
	https://camo.githubusercontent.com/583ab1044327ad645fec38b373832f325df8a7a2d07e8521f08de21b2b11a76f/68747470733a2f2f706c616365686f6c642e69742f31352f6666303030302f3030303030303f746578743d2b
Context Encoders: Feature Learning by Inpainting (2016)	https://arxiv.org/abs/1604.07379
[github]	https://github.com/pathak22/context-encoder
	https://camo.githubusercontent.com/519d692b3d9230ef9862b80177b3c1dd4a7fcc77b30e582b1912e5654654b567/68747470733a2f2f692e70696e696d672e636f6d2f353634782f35372f62652f64352f35376265643538356561393930623835383331346639313964623566633532322e6a7067
Compositional GAN: Learning Conditional Image Composition (2018)	https://arxiv.org/abs/1807.07560
	https://camo.githubusercontent.com/38f24ec26a4a0978d4d956cc5ec19e4fe0691d59bace5f857d2a58aa64eaf517/687474703a2f2f7062732e7477696d672e636f6d2f6d656469612f4469376c576457586f4141686436422e6a7067
[link]	https://arxiv.org/abs/1811.10597v
https://gandissect.csail.mit.edu/	https://gandissect.csail.mit.edu/
[link]	https://arxiv.org/abs/1704.03976
	https://camo.githubusercontent.com/4e3d1db4e26819bf4cf7745523b8a7642821bb79b95bb2f50c5dcf324b4ecc4b/68747470733a2f2f7777772e7265736561726368676174652e6e65742f70726f66696c652f54616b6572755f4d697961746f2f7075626c69636174696f6e2f3331363039383537312f6669677572652f666967322f41533a36363737393137353334393836333540313533363232353336393931382f44656d6f6e7374726174696f6e2d6f662d686f772d6f75722d5641542d776f726b732d6f6e2d73656d692d737570657276697365642d6c6561726e696e672d57652d67656e6572617465642d382d6c6162656c65642e706e67
	https://camo.githubusercontent.com/8781c97b711053eef5cc4dbdc3d83de11e683525b524976dc7058a327240bc34/68747470733a2f2f7068696c6c6970692e6769746875622e696f2f706978327069782f696d616765732f7465617365725f76332e6a7067
Pros and Cons of GAN Evaluation Measures (2018)	https://arxiv.org/abs/1802.03446
	https://patch-diff.githubusercontent.com/axruff/TransferLearning#domain-randomization
Lil'Log blog post titled - Domain Randomization for Sim2Real Transfer	https://lilianweng.github.io/lil-log/2019/05/05/domain-randomization.html#uniform-domain-randomization
	https://patch-diff.githubusercontent.com/axruff/TransferLearning#uniform-randomization
2017 - Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World	https://arxiv.org/abs/1703.06907
	https://camo.githubusercontent.com/94e91b29a597894c238b9aecf8f844c7107abe00d901d1f73bc79aad723f7f9f/68747470733a2f2f692e70696e696d672e636f6d2f353634782f39392f65392f36352f39396539363537333832656631653934653261636239353863376331636636632e6a7067
2017 - Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization	https://arxiv.org/abs/1804.06516
	https://camo.githubusercontent.com/583ab1044327ad645fec38b373832f325df8a7a2d07e8521f08de21b2b11a76f/68747470733a2f2f706c616365686f6c642e69742f31352f6666303030302f3030303030303f746578743d2b
	https://camo.githubusercontent.com/d8cd49ed772e5b8c5e23cd3b36b15ec6d0a288f6961faa245941a029b4e17a29/68747470733a2f2f72657365617263682e6e76696469612e636f6d2f73697465732f64656661756c742f66696c65732f7075626c69636174696f6e732f637670722d666967315f646f776e342e706e67
2017 - Sim-to-Real Transfer of Robotic Control with Dynamics Randomization	https://arxiv.org/abs/1710.06537
	https://camo.githubusercontent.com/09ba4e414f7e9047e6a8ffdc97f2b94c453a9fae6c4cf01ebcf30a459afb1aae/68747470733a2f2f786270656e672e6769746875622e696f2f70726f6a656374732f53696d546f5265616c2f73696d746f7265616c5f7465617365722e706e67
	https://patch-diff.githubusercontent.com/axruff/TransferLearning#guided-randomization
	https://patch-diff.githubusercontent.com/axruff/TransferLearning#optimization-for-task-performance
2018 - Learning To Simulate	https://arxiv.org/abs/1810.02513
	https://camo.githubusercontent.com/a04b3effb3b5790f3e9e0892728ff6765fcde2053f1197da37577107272431da/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f323030302f312a666869436b67334474324535517955564a4530527a512e706e67
2019 - [Meta-Sim]: Learning to Generate Synthetic Datasets	https://arxiv.org/abs/1904.11621
[website]	https://nv-tlabs.github.io/meta-sim/
	https://camo.githubusercontent.com/583ab1044327ad645fec38b373832f325df8a7a2d07e8521f08de21b2b11a76f/68747470733a2f2f706c616365686f6c642e69742f31352f6666303030302f3030303030303f746578743d2b
	https://camo.githubusercontent.com/7e591a29ce4cd892b9376c9077e4d6a5b3e71bef67d717926b851a2458eec0b7/68747470733a2f2f6e762d746c6162732e6769746875622e696f2f6d6574612d73696d2f7265736f75726365732f6d6574612d73696d2d7465617365722e706e67
	https://patch-diff.githubusercontent.com/axruff/TransferLearning#match-real-data-distribution
2019 - Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience	https://arxiv.org/abs/1810.05687
	https://camo.githubusercontent.com/e5c55be171c46ab5fbbdedd4d20229400f05e1054c428bff7b97664ea8aa3b2e/68747470733a2f2f646565706c6561726e2e6f72672f61727869765f66696c65732f313831302e303536383776312f78312e706e67
2018 - [RCAN] Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks	https://arxiv.org/abs/1812.07252
	https://camo.githubusercontent.com/583ab1044327ad645fec38b373832f325df8a7a2d07e8521f08de21b2b11a76f/68747470733a2f2f706c616365686f6c642e69742f31352f6666303030302f3030303030303f746578743d2b
	https://camo.githubusercontent.com/ab594fce4f967b4e1ca01bb15899b381a4de42bb9ecb7e4eb16651458241f035/68747470733a2f2f6c696c69616e77656e672e6769746875622e696f2f6c696c2d6c6f672f6173736574732f696d616765732f5243414e2e706e67
	https://patch-diff.githubusercontent.com/axruff/TransferLearning#guided-by-data-in-simulation
2019 - [DeceptionNet]: Network-Driven Domain Randomization	https://arxiv.org/abs/1904.02750
	https://camo.githubusercontent.com/b6b4f7ae350b65c7027497843f477dfc7deb7671433b42b7c4dfee669dee3cf6/68747470733a2f2f706c616365686f6c642e69742f31352f3030666630302f3030303030303f746578743d2b
	https://camo.githubusercontent.com/82137f11f6c0cb227cbe2d63c26629a3ef325a98fbd3b631268275425440f975/68747470733a2f2f6c696c69616e77656e672e6769746875622e696f2f6c696c2d6c6f672f6173736574732f696d616765732f646563657074696f6e2d6e65742e706e67
2019 - [ADR]: Active Domain Randomization	https://arxiv.org/abs/1904.04762
	https://camo.githubusercontent.com/583ab1044327ad645fec38b373832f325df8a7a2d07e8521f08de21b2b11a76f/68747470733a2f2f706c616365686f6c642e69742f31352f6666303030302f3030303030303f746578743d2b
	https://camo.githubusercontent.com/29e064e4b5b75c085339ac8d68bb0dd72465c8f14ada389c318ee28727aa774e/68747470733a2f2f6c696c69616e77656e672e6769746875622e696f2f6c696c2d6c6f672f6173736574732f696d616765732f4144522e706e67
	https://patch-diff.githubusercontent.com/axruff/TransferLearning#style-transfer
2016 - Image Style Transfer Using Convolutional Neural Networks	https://ieeexplore.ieee.org/document/7780634
2016 Perceptual losses for real-time style transfer and super-resolution	https://arxiv.org/abs/1603.08155?context=cs
[github]	https://github.com/jcjohnson/fast-neural-style
	https://camo.githubusercontent.com/583ab1044327ad645fec38b373832f325df8a7a2d07e8521f08de21b2b11a76f/68747470733a2f2f706c616365686f6c642e69742f31352f6666303030302f3030303030303f746578743d2b
	https://raw.githubusercontent.com/sunshineatnoon/Paper-Collection/master/images/RTNS.png
2017 - [CycleGAN] Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks	https://arxiv.org/abs/1703.10593
	https://camo.githubusercontent.com/f71c3e57a18abccf81d508c2589114c0e8939f308626b42cf2e54453a9892543/68747470733a2f2f6a756e79616e7a2e6769746875622e696f2f4379636c6547414e2f696d616765732f7465617365722e6a7067
2016 - [pix2pix] Image-to-Image Translation with Conditional Adversarial Networks	https://arxiv.org/abs/1611.07004
[github]	https://phillipi.github.io/pix2pix/
2018 - [DRIT] Diverse Image-to-Image Translation via Disentangled Representations	https://arxiv.org/abs/1808.00948
[github]	https://github.com/HsinYingLee/DRIT
	https://camo.githubusercontent.com/583ab1044327ad645fec38b373832f325df8a7a2d07e8521f08de21b2b11a76f/68747470733a2f2f706c616365686f6c642e69742f31352f6666303030302f3030303030303f746578743d2b
	https://camo.githubusercontent.com/73e32b84546b3ae5ac271fc63c13c28c91096eefad7193f67cb8ba5d5059c75c/68747470733a2f2f6d656469612e737072696e6765726e61747572652e636f6d2f6f726967696e616c2f737072696e6765722d7374617469632f696d6167652f63687025334131302e313030372532463937382d332d3033302d30313234362d355f332f4d656469614f626a656374732f3437343137325f315f456e5f335f466967335f48544d4c2e676966
2018 - A Style-Based Generator Architecture for Generative Adversarial Networks	https://arxiv.org/abs/1812.04948
[github]	https://github.com/NVlabs/stylegan
	https://github.com/NVlabs/stylegan/raw/master/stylegan-teaser.png
2018 - Deep Painterly Harmonization	https://arxiv.org/abs/1804.03189v3
[github]	https://github.com/luanfujun/deep-painterly-harmonization
	https://camo.githubusercontent.com/61bdf0d364d539fae6375e37654c78012c44635f428bedea53e3a01e33c7d3ea/68747470733a2f2f692e70696e696d672e636f6d2f353634782f66362f66612f37342f66366661373430333935633162393965636565326237316634366231363735312e6a7067
2021 - StyleLess layer: Improving robustness for real-world driving	https://www.semanticscholar.org/paper/StyleLess-layer%3A-Improving-robustness-for-driving-Rebut-Bursuc/4f8a4bd77795d9794047e2ec5b1ca35b016ed688
	https://camo.githubusercontent.com/133110c91b1f8e522ae7f4cb342f0347558467691a129e8aeb0816f631080791/68747470733a2f2f692e70696e696d672e636f6d2f353634782f31612f33302f31302f31613330313033303535383666373963383564653637636339316434336165352e6a7067
	https://patch-diff.githubusercontent.com/axruff/TransferLearning#texture-synthesis
2015 - Texture Synthesis Using Convolutional Neural Networks	https://arxiv.org/abs/1505.07376
[github]	https://mc.ai/tensorflow-implementation-of-paper-texture-synthesis-using-convolutional-neural-networks/
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http://bethgelab.org/deeptextures/	http://bethgelab.org/deeptextures/
https://www.textures.com/index.php	https://www.textures.com/index.php
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