- A PyTorch Implementation of DenseNet: Densely Connected Convolutional Networks, 1608.06993
- attention-is-all-you-need-pytorch: Attention Is All You Need, 1706.03762
- Attention Transfer: Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer, 1612.03928
- BEGAN in PyTorch: BEGAN: Boundary Equilibrium Generative Adversarial Networks, 1703.10717
- CycleGAN and pix2pix: CycleGAN and pix2pix in PyTorch, 1703.10593, 1611.07004
- Deformable Convolution: Deformable Convolution, 1703.06211
- DiscoGAN(1), DiscoGAN(2): Learning to Discover Cross-Domain Relations with Generative Adversarial Networks, 1703.05192
- diracnets: DiracNets: Training Very Deep Neural Networks Without Skip-Connections, 1706.00388
- dragan-pytorch, How to Train Your DRAGAN, 1705.07215
- Evolution Strategies: Evolution Strategies as a Scalable Alternative to Reinforcement Learning ,1703.03864
- Grad-CAM: Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization, 1610.02391
- Grammar Variational Autoencoder: Grammar Variational Autoencoder, 1703.01925
- pytorch-mobilenet: PyTorch MobileNet Implementation of "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications", 1704.04861
- neural-combinatorial-rl-pytorch: Neural Combinatorial Optimization with Reinforcement Learning, 1611.09940
- Neural Message Passing: Neural Message Passing for Quantum Chemistry, 1704.01212
- ODIN: Out-of-Distribution Detector for Neural Networks: Principled Detection of Out-of-Distribution Examples in Neural Networks, 1706.02690
- pytorch-fcn: PyTorch Implementation of Fully Convolutional Networks, 1605.06211
- PyTorch-Style-Transfer: Multi-style Generative Network for Real-time Transfer, 1703.06953 and Image Style Transfer Using Convolutional Neural Networks, pdf
- PyScatWave: Scaling the Scattering Transform, 1703.08961
- pytorch-pruning: Pruning Convolutional Neural Networks for Resource Efficient Inference, 1611.06440
- relational-networks: A simple neural network module for relational reasoning, 1706.01427
- Recurrent Variational Autoencoder: Recurrent Variational Autoencoder that generates sequential data implemented in pytorch, 1511.06349, 1508.06615
- TreeLSTM: Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks, 1503.00075
- VAE with a VampPrior: Variational Mixture of Posteriors, 1705.07120
- Visual Question Answering in Pytorch: MUTAN: Multimodal Tucker Fusion for Visual Question Answering, 1705.06676
- Wasserstein GAN: Wasserstein GAN, 1701.07875
- Weight Normalized GAN: On the Effects of Batch and Weight Normalization in Generative Adversarial Networks, 1704.03971
- YOLOv2 in PyTorch: PyTorch implementation of YOLOv2, 1612.08242
- Pytorch-Project-Template: A scalable template for PyTorch projects, with examples in Image Segmentation, Object classification, GANs and Reinforcement Learning.
- Deep Learning for NLP with PyTorch: An IPython Notebook tutorial on deep learning, with an emphasis on Natural Language Processing
- Practical PyTorch: Practical PyTorch tutorials, focused on using RNNs for NLP
- Pytorch Examples: This repository introduces the fundamental concepts of PyTorch through self-contained examples
- PyTorch Mini Tutorials: Minimal tutorials for PyTorch adapted from Alec Radford's Theano tutorials.
- Pytorch Official Examples: A repository showcasing examples of using pytorch
- Pytorch Official Tutorials: PyTorch Tutorials
- pytorch-playground: Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)
- Pytorch Tutorial: PyTorch Tutorials. Most of the models were implemented with less than 30 lines of code.
- PyTorch Tutorials: PyTorch tutorials better rendered with readthedocs style
- t-SNE experiments in pytorch: A simple t-SNE model implemented in pytorch
- Welcome tutorials: MILA's pytorch tutorials
- image-classification-mobile: Examples of reproducible training various classification models for ImageNet-1K
- Federated Learning with PyTorch: Tutorial about PyTorch and Flower creating a federated workload
- PyTorch to Keras model converter
- Gluon to PyTorch model converter with code generation
- MMdnn
- Flower: A Friendly Federated Learning Framework
- A Tour of PyTorch Internals
- Adversarial Autoencoders (with Pytorch)
- Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)
- Matrix Factorization in PyTorchn
- Recursive Neural Networks with PyTorch
- PyTorch: From Centralized to Federated
- Deep Learning with PyTorch: Deep Learning with PyTorch teaches you how to implement deep learning algorithms with Python and PyTorch, the book includes a case study: building an algorithm capable of detecting malignant lung tumors using CT scans.
- Serverless Machine Learning in Action with PyTorch and AWS: Serverless Machine Learning in Action is a guide to bringing your experimental PyTorch machine learning code to production using serverless capabilities from major cloud providers like AWS, Azure, or GCP.
- Programming PyTorch for Deep Learning: Shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text,and more through deep dives into each element.
- Deep Learning with PyTorch: Build neural network models in text, vision and advanced analytics using PyTorch.
- PyTorch vs TensorFlow: A reddit post about PyTorch and TensorFlow
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