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Title: GitHub - Tensor46/TensorMONK: A collection of deep learning models (PyTorch implemtation)

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Description: A collection of deep learning models (PyTorch implemtation) - Tensor46/TensorMONK

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Progressive Growing of GANshttps://arxiv.org/pdf/1710.10196.pdf
https://patch-diff.githubusercontent.com/Tensor46/TensorMONK#progressive-growing-of-gans
https://github.com/Tensor46/TensorMONK/blob/develop/models/pggan-cifar10-level1.gif
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https://patch-diff.githubusercontent.com/Tensor46/TensorMONK#references
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Maxout Networkshttps://arxiv.org/pdf/1302.4389.pdf
SWISH: A SELF-GATED ACTIVATION FUNCTIONhttps://arxiv.org/pdf/1710.05941v1.pdf
Mish: Self Regularized Non-Monotonic Activation Functionhttps://arxiv.org/abs/1908.08681
https://patch-diff.githubusercontent.com/Tensor46/TensorMONK#classification
Deep Neural Decision Forestshttps://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Kontschieder_Deep_Neural_Decision_ICCV_2015_paper.pdf
https://patch-diff.githubusercontent.com/Tensor46/TensorMONK#generative-models
Auto-Encoding Variational Bayeshttps://arxiv.org/pdf/1312.6114v10.pdf
Progressive Growing of GANshttps://arxiv.org/pdf/1710.10196.pdf
Self-Attention Generative Adversarial Networkshttps://arxiv.org/pdf/1805.08318.pdf
https://patch-diff.githubusercontent.com/Tensor46/TensorMONK#image-recognition-models
Aggregated Residual Transformations for Deep Neural Networkshttps://arxiv.org/pdf/1611.05431.pdf
Deep Residual Learning for Image Recognitionhttps://arxiv.org/pdf/1512.03385.pdf
Densely Connected Convolutional Networkshttps://arxiv.org/pdf/1608.06993.pdf
Dynamic Routing Between Capsuleshttps://arxiv.org/pdf/1710.09829.pdf
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkshttps://arxiv.org/pdf/1905.11946.pdf
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learninghttps://arxiv.org/pdf/1602.07261.pdf
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationshttps://arxiv.org/pdf/1704.04861.pdf
MobileNetV2: Inverted Residuals and Linear Bottleneckshttps://arxiv.org/pdf/1801.04381.pdf
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutionshttps://arxiv.org/pdf/1711.08141.pdf
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Deviceshttps://arxiv.org/pdf/1707.01083.pdf
Squeeze-and-Excitation Networkshttps://arxiv.org/pdf/1709.01507.pdf
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AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomyhttps://arxiv.org/pdf/1808.05238.pdf
ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-timehttps://arxiv.org/pdf/1805.04554.pdf
U-Net: Convolutional Networks for Biomedical Image Segmentationhttps://arxiv.org/pdf/1505.04597.pdf
https://patch-diff.githubusercontent.com/Tensor46/TensorMONK#local-features
Learning Discriminative and Transformation Covariant Local Feature Detectorshttp://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Learning_Discriminative_and_CVPR_2017_paper.pdf
https://patch-diff.githubusercontent.com/Tensor46/TensorMONK#loss-functions
AN EXPLORATION OF SOFTMAX ALTERNATIVES BELONGING TO THE SPHERICAL LOSS FAMILYhttps://arxiv.org/pdf/1511.05042.pdf
Analyzing and Improving Representations with the Soft Nearest Neighbor Losshttps://arxiv.org/pdf/1902.01889.pdf
ArcFace: Additive Angular Margin Loss for Deep Face Recognitionhttps://arxiv.org/pdf/1801.07698.pdf
CosFace: Large Margin Cosine Loss for Deep Face Recognitionhttps://arxiv.org/pdf/1801.09414.pdf
Rethinking Feature Distribution for Loss Functions in Image Classificationhttps://arxiv.org/pdf/1803.02988.pdf
Tversky loss function for image segmentation using 3D fully convolutional deep networkshttps://arxiv.org/pdf/1706.05721.pdf
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SSD: Single Shot MultiBox Detectorhttps://arxiv.org/pdf/1512.02325.pdf
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Lookahead Optimizer: k steps forward, 1 step backhttps://arxiv.org/pdf/1907.08610.pdf
On the Variance of the Adaptive Learning Rate and Beyondhttps://arxiv.org/pdf/1908.03265v1.pdf
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DropBlock: A regularization method for convolutional networkshttps://arxiv.org/pdf/1810.12890.pdf
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