Python Convolutional-neural-networks Resources

GCond A PyTorch implementation of paper "Graph Condensation for Graph Neural Networks" Code will be released soon. Stay tuned :) Abstract We propose a

Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision Training Efficiency We show the training efficiency of our DSLP model b

Progressive Image Deraining Networks: A Better and Simpler Baseline [arxiv] [pdf] [supp] Introduction This paper provides a better and simpler baselin

SRNTT: Image Super-Resolution by Neural Texture Transfer Tensorflow implementation of the paper Image Super-Resolution by Neural Texture Transfer acce

About PyTorch 1.2.0 Now the master branch supports PyTorch 1.2.0 by default. Due to the serious version problem (especially torch.utils.data.dataloade

IGNN Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" [paper] [supp] Prepare datasets 1 Download training dataset

EDVR has been merged into BasicSR. This GitHub repo is a mirror of BasicSR. Recommend to use BasicSR, and open issues, pull requests, etc in BasicSR.

Overview The Open Graph Benchmark (OGB) is a collection of benchmark datasets, data loaders, and evaluators for graph machine learning. Datasets cover

Graph Convolutional Networks This is a TensorFlow implementation of Graph Convolutional Networks for the task of (semi-supervised) classification of n

Simplifying Graph Convolutional Networks Updates As pointed out by #23, there was a subtle bug in our preprocessing code for the reddit dataset. After

Intro This repository contains code to generate data and reproduce experiments from our NeurIPS 2019 paper: Boris Knyazev, Graham W. Taylor, Mohamed R

Graph Convolutional Networks for Temporal Action Localization This repo holds the codes and models for the PGCN framework presented on ICCV 2019 Graph

IsoGCN The official implementation of IsoGCN, presented in the ICLR2021 paper Isometric Transformation Invariant and Equivariant Graph Convolutional N

Simple and Deep Graph Convolutional Networks This repository contains a PyTorch implementation of "Simple and Deep Graph Convolutional Networks".(http

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks This repository contains a TensorFlow implementation of "

When Does Self-Supervision Help Graph Convolutional Networks? PyTorch implementation for When Does Self-Supervision Help Graph Convolutional Networks?

DropEdge: Towards Deep Graph Convolutional Networks on Node Classification This is a Pytorch implementation of paper: DropEdge: Towards Deep Graph Con

Benchmarking Graph Neural Networks Updates Nov 2, 2020 Project based on DGL 0.4.2. See the relevant dependencies defined in the environment yml files

GAT Graph Attention Networks (Veličković et al., ICLR 2018): https://arxiv.org/abs/1710.10903 GAT layer t-SNE + Attention coefficients on Cora Overvie

DeepGCNs: Can GCNs Go as Deep as CNNs? In this work, we present new ways to successfully train very deep GCNs. We borrow concepts from CNNs, mainly re

Densely Connected Convolutional Networks (DenseNets) This repository contains the code for DenseNet introduced in the following paper Densely Connecte

Feedback Convolutional Neural Network for Visual Localization and Segmentation The code is an implementation of Feedback Convolutional Neural Network

Feedback Networks http://feedbacknet.stanford.edu/ Paper: Feedback Networks, CVPR 2017. Amir R. Zamir*,Te-Lin Wu*, Lin Sun, William B. Shen, Bertram E

Deep Residual Networks with 1K Layers By Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. Microsoft Research Asia (MSRA). Table of Contents Introduc

At Bittensor, we are creating an open, decentralized, peer-to-peer network that functions as a market system for the development of artificial intelligence.

SynNet This repo contains the code and analysis scripts for our amortized approach to synthetic tree generation using neural networks. Our model can s

This is a repository for the paper of "Well-classified Examples are Underestimated in Classification with Deep Neural Networks" The implementation and

Example of a Quantum-enhanced transformer neural network Get the code: git clone https://github.com/rdisipio/qtransformer.git cd qtransformer Create

SSSNET SSSNET: Semi-Supervised Signed Network Clustering For details, please read our paper. Environment Setup Overview The project has been tested on

Fast Fourier Convolution (FFC) for Image Classification This is the official code of Fast Fourier Convolution for image classification on ImageNet. Ma

PyTorch implementation of PP-LCNet Reproduction of PP-LCNet architecture as described in PP-LCNet: A Lightweight CPU Convolutional Neural Network by C

Boolean Prompting for Neural Text Generators Neural text generators like the GPT models promise a general-purpose means of manipulating texts. These m

MosaicML Composer MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training. We aim to ease th

Neural Motion Learner Introduction This work is to extract skeletal structure from volumetric observations and to learn motion dynamics from the detec

DINN We introduce Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, a

EchoVPR Repo for EchoVPR: Echo State Networks for Visual Place Recognition Currently under development Dirs: data: pre-collected hidden representation

Select, Reduce, Connect This repository contains the code used for the experiments of: "Understanding Pooling in Graph Neural Networks" Setup Install

Instance-based Label Smoothing for Neural Networks Pytorch Implementation of the algorithm. This repository includes a new proposed method for instanc

Recurrent Model-Free RL is a Strong Baseline for Many POMDPs Welcome to the POMDP world! This repo provides some simple baselines for POMDPs, specific

Disturbing Target Values for Neural Network regularization: attacking the loss layer to prevent overfitting 1. Classification Task PyTorch implementat

Doubly Trained Neural Machine Translation System for Adversarial Attack and Data Augmentation Languages Experimented: Data Overview: Source Target Tra

Multi-modal Interaction Graph Convolutioal Network for Temporal Language Localization in Videos Official implementation for Multi-Modal Interaction Gr

3D Infomax improves GNNs for Molecular Property Prediction Video | Paper We pre-train GNNs to understand the geometry of molecules given only their 2D

Style-based Drum Synthesis with GAN Inversion Demo TensorFlow implementation of a style-based version of the adversarial drum synth (ADS) from the pap

Select, Reduce, Connect This repository contains the code used for the experiments of: "Understanding Pooling in Graph Neural Networks" Setup Install

UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction Project Page | Paper | Supplementary | Video This reposit

AASIST This repository provides the overall framework for training and evaluating audio anti-spoofing systems proposed in 'AASIST: Audio Anti-Spoofing

This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.

RetrievalFuse Paper | Project Page | Video RetrievalFuse: Neural 3D Scene Reconstruction with a Database Yawar Siddiqui, Justus Thies, Fangchang Ma, Q

Written in Python using the Azure Speech SDK. App.py provides an easy way to create an Text-To-Speech request to Azure Speech and download the wav file.

Alias-Free Generative Adversarial Networks (StyleGAN3) Official PyTorch implementation

Permute Me Softly: Learning Soft Permutations for Graph Representations

HyperNets Hypercomplex Neural Networks with PyTorch: this repository would be a container for hypercomplex neural network modules to facilitate resear

Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network This repository is the official implementation of Speech Separati

PortaSpeech - PyTorch Implementation PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech. Model Size Module Nor

GndNet: Fast Ground plane Estimation and Point Cloud Segmentation for Autonomous Vehicles. Authors: Anshul Paigwar, Ozgur Erkent, David Sierra Gonzale

-IEEE-TIM-2021-1-Shallow-CNN-for-HAR [IEEE TIM 2021-1] Shallow Convolutional Neural Networks for Human Activity Recognition using Wearable Sensors All

Neural Network For Gender Recognition How to test it? Install requirements.txt file using pip install -r requirements.txt command Run nn.py using pyth

A graph neural network (GNN) model to predict protein-protein interactions (PPI) with no sample features

A geometric deep learning pipeline for predicting protein interface contacts.

A ultra-lightweight 3D renderer of the Tensorflow/Keras neural network architectures

CoolAvatars Welcome to this repository of CoolAvatars. Using this project, you can generate cool avatars not only from the samples present in my image

HeatNet HeatNet is a python package that provides tools to build, train and evaluate neural networks designed to predict extreme heat wave events glob

ChessCoach is a neural network-based chess engine capable of natural-language commentary.

GNNLens2 is an interactive visualization tool for graph neural networks (GNN).

Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces(ICML 2021) This repository contains the code

Neural Splines: Fitting 3D Surfaces with Inifinitely-Wide Neural Networks This repository contains the official implementation of the CVPR 2021 (Oral)

PortaSpeech - PyTorch Implementation PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech. Model Size Module Nor

TensorFuzz: Coverage Guided Fuzzing for Neural Networks This repository contains a library for performing coverage guided fuzzing of neural networks,

PhyCRNet Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs Paper link: [ArXiv] By: Pu Ren, Chengping Rao, Yang

nerf-pytorch A PyTorch re-implementation Project | Video | Paper NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis Ben Mildenhall

NeRF: Neural Radiance Fields Project Page | Video | Paper | Data Tensorflow implementation of optimizing a neural representation for a single scene an

Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance Project Page | Paper | Data This repository contains an implementatio

GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields Project Page | Paper | Supplementary | Video | Slides | Blog | Talk If

D-NeRF: Neural Radiance Fields for Dynamic Scenes [Project] [Paper] D-NeRF is a method for synthesizing novel views, at an arbitrary point in time, of

pixelNeRF: Neural Radiance Fields from One or Few Images Alex Yu, Vickie Ye, Matthew Tancik, Angjoo Kanazawa UC Berkeley arXiv: http://arxiv.org/abs/2

NeX: Real-time View Synthesis with Neural Basis Expansion Project Page | Video | Paper | COLAB | Shiny Dataset We present NeX, a new approach to novel

arc-pytorch PyTorch implementation of Attentive Recurrent Comparators by Shyam et al. A blog explaining Attentive Recurrent Comparators Visualizing At

RNN-for-Joint-NLU Pytorch implementation of "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling"

ON-LSTM This repository contains the code used for word-level language model and unsupervised parsing experiments in Ordered Neurons: Integrating Tree

News SRU++, a new SRU variant, is released. [tech report] [blog] The experimental code and SRU++ implementation are available on the dev branch which

Kaolin: A Pytorch Library for Accelerating 3D Deep Learning Research Overview NVIDIA Kaolin library provides a PyTorch API for working with a variety

A 3D multi-modal medical image segmentation library in PyTorch We strongly believe in open and reproducible deep learning research. Our goal is to imp

NeRF-pytorch NeRF (Neural Radiance Fields) is a method that achieves state-of-the-art results for synthesizing novel views of complex scenes. Here are

Awesome-of-Neural-Rendering A curated list of neural rendering and related resources. Please feel free to pull requests or open an issue to add papers

α-Indirect Control in Onion-like Networks

Educational python for Neural Networks, written in pure Python/NumPy.

ZH-EN NMT Chinese to English Neural Machine Translation This project is inspired by Stanford's CS224N NMT Project Dataset used in this project: News C

Deep-Unsupervised-Domain-Adaptation Pytorch implementation of four neural network based domain adaptation techniques: DeepCORAL, DDC, CDAN and CDAN+E.

Official code release for ICCV 2021 paper SNARF: Differentiable Forward Skinning for Animating Non-rigid Neural Implicit Shapes.

Adaptive Task-Relational Context (ATRC) This repository provides source code for the ICCV 2021 paper Exploring Relational Context for Multi-Task Dense

Abstract DeeProb-kit is a Python library that implements deep probabilistic models such as various kinds of Sum-Product Networks, Normalizing Flows an

A curated (most recent) list of resources for Learning with Noisy Labels

SlowFast A PyTorch implementation of SlowFast based on ICCV 2019 paper SlowFast Networks for Video Recognition. Requirements Anaconda PyTorch conda in

face.evoLVe: High-Performance Face Recognition Library based on PaddlePaddle & PyTorch Evolve to be more comprehensive, effective and efficient for fa

Differentiable Architecture Search Code accompanying the paper DARTS: Differentiable Architecture Search Hanxiao Liu, Karen Simonyan, Yiming Yang. arX

DenseNAS The code of the CVPR2020 paper Densely Connected Search Space for More Flexible Neural Architecture Search. Neural architecture search (NAS)

Maximum Entropy Generators for Energy-Based Models All experiments have tensorboard visualizations for samples / density / train curves etc. To run th

Learning Confidence Estimates for Neural Networks This repository contains the code for the paper Learning Confidence for Out-of-Distribution Detectio

Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization This code implements the g