A Variational Autoencoder based on the ResNet18-architecture

Official code for "Diversity-Measurable Anomaly Detection", CVPR 2023 by Wenrui Liu, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen.

Official pytorch re-implementation of "Demystifying Inter-Class Disentanglement", ICLR 2020.

Pytorch implementation of Improved Deep Embedded Clustering

A Pytorch implementation of DSC-Net (Deep subspace clustering networks, NIPS17)

Code for the paper "Adversarially Regularized Autoencoders (ICML 2018)" by Zhao, Kim, Zhang, Rush and LeCun

Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners

A PyTorch implementation of the Deep SVDD anomaly detection method

Repository for the Deep One-Class Classification ICML 2018 paper

A PyTorch implementation of DenseNet.

Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs

Adversarial Graph Augmentation to Improve Graph Contrastive Learning

Implementation of MolCLR: "Molecular Contrastive Learning of Representations via Graph Neural Networks" in PyG.

A curated list of research papers related to learning disentangled representations

Prediction of PPB(Plasma Protein Binding) With the help of Graph Neural Network(GNNs)

DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction

Source code for "Factorizable Graph Convolutional Networks", NeurIPS'20

GraphDTA: Predicting drug-target binding affinity with graph neural networks

Graph Neural Networks for Quantum Chemistry

BAPA preprocessing scripts and model

Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集

Must-read papers on graph neural networks (GNN)

NeurIPS 2018 MLMM Workshop: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction

Crystal graph convolutional neural networks for predicting material properties.