mcohen01 / the-incredible-pytorch

The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

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This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch. Feel free to make a pull request to contribute to this list.

Tutorials

• Official PyTorch Tutorials
• Official PyTorch Examples
• Practical Deep Learning with PyTorch by Deep Learning Wizard (Ritchie Ng)
• Simple Examples to Introduce PyTorch
• Mini Tutorials in PyTorch
• Deep Learning for NLP
• Deep Learning Tutorial for Researchers
• Fully Convolutional Networks implemented with PyTorch
• Simple PyTorch Tutorials Zero to ALL
• DeepNLP-models-Pytorch
• MILA PyTorch Welcome Tutorials
• Practical PyTorch
• PyTorch Project Template
  • A scalable template for PyTorch projects, with examples in Image Segmentation, Object classification, GANs and Reinforcement Learning.
  • Get started Tutorial
  • Mnist Tutorial
  • ERFNET
  • DCGAN
  • DQN

Visualization

• Loss Visualization
• Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
• Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
• SmoothGrad: removing noise by adding noise

Explainability

• Shap, a unified approach to explain the output of any machine learning model

Object Detection

• YOLOv3
• YOLOv2: Real-Time Object Detection
• SSD: Single Shot MultiBox Detector
• Detectron models for Object Detection
• Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
• Whale Detector

Optimization

• Riemannian Adaptive Optimization Methods
• L-BFGS
• OptNet: Differentiable Optimization as a Layer in Neural Networks
• Learning to learn by gradient descent by gradient descent

Facial, Action and Pose Recognition

• High performance facial recognition library on PyTorch
• FaceBoxes, a CPU real-time face detector with high accuracy
• How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
• Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition
• PyTorch Realtime Multi-Person Pose Estimation
• SphereFace: Deep Hypersphere Embedding for Face Recognition
• GANimation: Anatomically-aware Facial Animation from a Single Image
• Shufflenet V2 by Face++ with better results than paper
• Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
• Unsupervised Learning of Depth and Ego-Motion from Video
• FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
• FlowNet: Learning Optical Flow with Convolutional Networks
• Optical Flow Estimation using a Spatial Pyramid Network
• OpenFace in PyTorch

Super resolution

• Enhanced Deep Residual Networks for Single Image Super-Resolution
• Superresolution using an efficient sub-pixel convolutional neural network
• Perceptual Losses for Real-Time Style Transfer and Super-Resolution

Medical

• Delira, lightweight framework for medical imaging prototyping
• V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
• Medical Torch, medical imaging framework for PyTorch

3D Segmentation, Classification and Regression

• PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

Video Recognition

• Deep Video Analytics

Recurrent Neural Networks (RNNs)

• Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
• Averaged Stochastic Gradient Descent with Weight Dropped LSTM
• Training RNNs as Fast as CNNs
• Quasi-Recurrent Neural Network (QRNN)
• ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation
• A Recurrent Latent Variable Model for Sequential Data (VRNN)
• Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
• Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
• Attentive Recurrent Comparators
• Collection of Sequence to Sequence Models with PyTorch
  1. Vanilla Sequence to Sequence models
  2. Attention based Sequence to Sequence models
  3. Faster attention mechanisms using dot products between the final encoder and decoder hidden states

Convolutional Neural Networks (CNNs)

• Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks
• Faster Faster R-CNN Implementation
  • Faster R-CNN Another Implementation
• Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer
• Wide ResNet model in PyTorch -DiracNets: Training Very Deep Neural Networks Without Skip-Connections
• An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
• Efficient Densenet
• Video Frame Interpolation via Adaptive Separable Convolution
• Learning local feature descriptors with triplets and shallow convolutional neural networks
• Densely Connected Convolutional Networks
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
• Deep Residual Learning for Image Recognition
• Training Wide ResNets for CIFAR-10 and CIFAR-100 in PyTorch
• Deformable Convolutional Network
• Convolutional Neural Fabrics
• Deformable Convolutional Networks in PyTorch
• Dilated ResNet combination with Dilated Convolutions
• Striving for Simplicity: The All Convolutional Net
• Convolutional LSTM Network
• Big collection of pretrained classification models
• PyTorch Image Classification with Kaggle Dogs vs Cats Dataset
• CIFAR-10 on Pytorch with VGG, ResNet and DenseNet
• Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)

Segmentation

• Pixel-wise Segmentation on VOC2012 Dataset using PyTorch

Graph

• Graph Wavelet Neural Network
• Watch Your Step: Learning Node Embeddings via Graph Attention
• Signed Graph Convolutional Network
• Graph Classification Using Structural Attention

Multi-task Learning

• Hierarchical Multi-Task Learning Model
• Task-based End-to-end Model Learning

GANs, VAEs, and AEs

• Progressive Growing of GANs for Improved Quality, Stability, and Variation
• Wasserstein GAN
• Adversarial Generator-Encoder Network
• Image-to-Image Translation with Conditional Adversarial Networks
• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
• On the Effects of Batch and Weight Normalization in Generative Adversarial Networks
• Improved Training of Wasserstein GANs
• Collection of Generative Models with PyTorch
  • Generative Adversarial Nets (GAN)
    1. Vanilla GAN
    2. Conditional GAN
    3. InfoGAN
    4. Wasserstein GAN
    5. Mode Regularized GAN
  • Variational Autoencoder (VAE)
    1. Vanilla VAE
    2. Conditional VAE
    3. Denoising VAE
    4. Adversarial Autoencoder
    5. Adversarial Variational Bayes
• Improved Training of Wasserstein GANs
• CycleGAN and Semi-Supervised GAN
• Improving Variational Auto-Encoders using Householder Flow and using convex combination linear Inverse Autoregressive Flow
• PyTorch GAN Collection
• Generative Adversarial Networks, focusing on anime face drawing
• Simple Generative Adversarial Networks
• Adversarial Auto-encoders
• torchgan: Framework for modelling Generative Adversarial Networks in Pytorch

Adversarial Attacks

• Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
• Explaining and Harnessing Adversarial Examples

Style Transfer

• A Neural Algorithm of Artistic Style
• Multi-style Generative Network for Real-time Transfer
• DeOldify, Coloring Old Images
• Neural Style Transfer
• Fast Neural Style Transfer
• Draw like Bob Ross

Image Captioning

• Neuraltalk 2, Image Captioning Model, in PyTorch
• Generate captions from an image with PyTorch

Transformers

• Attention is all you need
• Spatial Transformer Networks

Similarity Networks and Functions

• Conditional Similarity Networks

Reasoning

• Inferring and Executing Programs for Visual Reasoning

General NLP

• BERT
• VSE++: Improved Visual-Semantic Embeddings
• A Structured Self-Attentive Sentence Embedding
• Neural Sequence labeling model
• SINE: Scalable Incomplete Network Embedding
• Skip-Thought Vectors
• Complete Suite for Training Seq2Seq Models in PyTorch
• MUSE: Multilingual Unsupervised and Supervised Embeddings

Question and Answering

• Visual Question Answering in Pytorch
• Reading Wikipedia to Answer Open-Domain Questions
• Deal or No Deal? End-to-End Learning for Negotiation Dialogues
• Interpretable Counting for Visual Question Answering
• Open Source Chatbot with PyTorch

Speech Generation and Recognition

• PyTorch-Kaldi Speech Recognition Toolkit
• WaveGlow: A Flow-based Generative Network for Speech Synthesis
• OpenNMT
• Deep Speech 2: End-to-End Speech Recognition in English and Mandarin

Document and Text Classification

• Hierarchical Attention Network for Document Classification
• Hierarchical Attention Networks for Document Classification
• CNN Based Text Classification

Text Generation

• Pytorch Poetry Generation

Translation

• Open-source (MIT) Neural Machine Translation (NMT) System

Sentiment Analysis

• Recurrent Neural Networks for Sentiment Analysis (Aspect-Based) on SemEval 2014
• Seq2Seq Intent Parsing

Deep Reinforcement Learning

• Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
• Asynchronous Methods for Deep Reinforcement Learning
• Continuous Deep Q-Learning with Model-based Acceleration
• Asynchronous Methods for Deep Reinforcement Learning for Atari 2600
• Trust Region Policy Optimization
• Neural Combinatorial Optimization with Reinforcement Learning
• Noisy Networks for Exploration
• Distributed Proximal Policy Optimization
• Reinforcement learning models in ViZDoom environment with PyTorch
• Reinforcement learning models using Gym and Pytorch
• SLM-Lab: Modular Deep Reinforcement Learning framework in PyTorch

Deep Bayesian Learning and Probabilistic Programmming

• Probabilistic Programming and Statistical Inference in PyTorch

Regression Types

• Quantile Regression DQN

Neural Network General Improvements

• In-Place Activated BatchNorm for Memory-Optimized Training of DNNs
• Train longer, generalize better: closing the generalization gap in large batch training of neural networks
• FreezeOut: Accelerate Training by Progressively Freezing Layers
• Binary Stochastic Neurons
• Pruning Convolutional Neural Networks for Resource Efficient Inference
• Compact Bilinear Pooling
• Mixed Precision Training in PyTorch

DNN Applications in Chemistry and Physics

• Neural Message Passing for Quantum Chemistry
• Automatic chemical design using a data-driven continuous representation of molecules
• Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge

New Thinking on General Neural Network Architecture

• Decoupled Neural Interfaces using Synthetic Gradients

To be Classified

• Perturbative Neural Networks
• Accurate Neural Network Potential
• Scaling the Scattering Transform: Deep Hybrid Networks
• Principled Detection of Out-of-Distribution Examples in Neural Networks
• CortexNet: a Generic Network Family for Robust Visual Temporal Representations
• Oriented Response Networks
• Associative Compression Networks
• Clarinet
• Continuous Wavelet Transforms
• mixup: Beyond Empirical Risk Minimization
• Network In Network
• Highway Networks
• Hybrid computing using a neural network with dynamic external memory
• Value Iteration Networks
• Differentiable Neural Computer
• A Neural Representation of Sketch Drawings
• Understanding Deep Image Representations by Inverting Them
• NIMA: Neural Image Assessment
• NASNet-A-Mobile. Ported weights
• Graphics code generating model using Processing

PyTorch Utilities

• PyTorch Inference on C++ in Windows
• EuclidesDB, Multi-Model Machine Learning Feature Database
• Data Augmentation and Sampling for Pytorch
• PyText, deep learning based NLP modelling framework officially maintained by FAIR
• Torchstat for Statistics on PyTorch Models
• Load Audio files directly into PyTorch Tensors
• Weight Initializations
• Spatial transformer implemented in PyTorch
• PyTorch AWS AMI, run PyTorch with GPU support in less than 5 minutes
• Use tensorboard with PyTorch
• Simple Fit Module in PyTorch, similar to Keras
• torchbearer: A model fitting library for PyTorch
• PyTorch to Keras model converter
• Gluon to PyTorch model converter with code generation

PyTorch Video Tutorials

• Practical Deep Learning with PyTorch
• PyTorch Zero to All Lectures

Community

• PyTorch Discussion Forum
• StackOverflow PyTorch Tags

Links to This Repository

• Github Repository
• Website

Contributions

Do feel free to contribute!

You can raise an issue or submit a pull request, whichever is more convenient for you. The guideline is simple: just follow the format of the previous bullet point.