the-incredible-pytorch
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.
A scalable template for PyTorch projects, with examples in Image Segmentation, Object classification, GANs and Reinforcement Learning.
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Shap, a unified approach to explain the output of any machine learning model
VIsualizing PyTorch saved .pth deep learning models with netron
Mask R-CNN Benchmark: Faster R-CNN and Mask R-CNN in PyTorch 1.0
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples
Principled Detection of Out-of-Distribution Examples in Neural Networks
Learning Confidence for Out-of-Distribution Detection in Neural Networks
RAdam, On the Variance of the Adaptive Learning Rate and Beyond
Over9000, Comparison of RAdam, Lookahead, Novograd, and combinations
OptNet: Differentiable Optimization as a Layer in Neural Networks
Tor10, generic tensor-network library for quantum simulation in PyTorch
PennyLane, cross-platform Python library for quantum machine learning with PyTorch interface
Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
Pruning Convolutional Neural Networks for Resource Efficient Inference
Pruning neural networks: is it time to nip it in the bud? (showing reduced networks work better)
Facenet: Pretrained Pytorch face detection and recognition models
Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition
GANimation: Anatomically-aware Facial Animation from a Single Image
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
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
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
Delira, lightweight framework for medical imaging prototyping
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
Averaged Stochastic Gradient Descent with Weight Dropped LSTM
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
Vanilla Sequence to Sequence models
Attention based Sequence to Sequence models
Faster attention mechanisms using dot products between the final encoder and decoder hidden states
LegoNet: Efficient Convolutional Neural Networks with Lego Filters
MeshCNN, a convolutional neural network designed specifically for triangular meshes
PyTorch Image Models, ResNet/ResNeXT, DPN, MobileNet-V3/V2/V1, MNASNet, Single-Path NAS, FBNet
Wide ResNet model in PyTorch -DiracNets: Training Very Deep Neural Networks Without Skip-Connections
Video Frame Interpolation via Adaptive Separable Convolution
Learning local feature descriptors with triplets and shallow convolutional neural networks
Very Deep Convolutional Networks for Large-Scale Image Recognition
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
PyTorch Image Classification with Kaggle Dogs vs Cats Dataset
Pywick - High-level batteries-included neural network training library for Pytorch
Improving Semantic Segmentation via Video Propagation and Label Relaxation
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Semi-Supervised Graph Classification: A Hierarchical Graph Perspective
PyTorch BigGraph by FAIR for Generating Embeddings From Large-scale Graph Data
Splitter: Learning Node Representations that Capture Multiple Social Contexts
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
Lorentz Embeddings: Learn Continuous Hierarchies in Hyperbolic Space
Watch Your Step: Learning Node Embeddings via Graph Attention
SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
A Style-Based Generator Architecture for Generative Adversarial Networks
Learning deep representations by mutual information estimation and maximization
Progressive Growing of GANs for Improved Quality, Stability, and Variation
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
Generative Adversarial Nets (GAN)
Variational Autoencoder (VAE)
Generative Adversarial Networks, focusing on anime face drawing
torchgan: Framework for modelling Generative Adversarial Networks in Pytorch
Espresso, Module Neural Automatic Speech Recognition Toolkit
Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification
Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading
WaveGlow: A Flow-based Generative Network for Speech Synthesis
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
Learning when to communicate at scale in multiagent cooperative and competitive tasks
Actor-Attention-Critic for Multi-Agent Reinforcement Learning
Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
Asynchronous Methods for Deep Reinforcement Learning for Atari 2600
Neural Combinatorial Optimization with Reinforcement Learning
Reinforcement learning models in ViZDoom environment with PyTorch
SLM-Lab: Modular Deep Reinforcement Learning framework in PyTorch
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
Probabilistic Programming and Statistical Inference in PyTorch
In-Place Activated BatchNorm for Memory-Optimized Training of DNNs
FreezeOut: Accelerate Training by Progressively Freezing Layers
Automatic chemical design using a data-driven continuous representation of molecules
Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge
CortexNet: a Generic Network Family for Robust Visual Temporal Representations
Hybrid computing using a neural network with dynamic external memory
Higher, obtain higher order gradients over losses spanning training loops
Layer-by-layer PyTorch Model Profiler for Checking Model Time Consumption
Diffdist, Adds Support for Differentiable Communication allowing distributed model parallelism
PyText, deep learning based NLP modelling framework officially maintained by FAIR
PyTorch AWS AMI, run PyTorch with GPU support in less than 5 minutes
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.
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