yc096

TorchSemiSeg

[CVPR 2021] CPS: Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

CCVC

[CVPR 2023] Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation

Awesome-Pretraining-for-Graph-Neural-Networks

A curated list of papers on pre-training for graph neural networks (Pre-train4GNN).

GraphSAGE-and-GAT-for-link-prediction

GraphSAGE and GAT for link prediction.

pytorch-image-models

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

segformer-pytorch

这是一个segformer-pytorch的源码，可以用于训练自己的模型。

Drug-Interaction-Research

awesome-self-supervised-gnn

Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).

GRACE

[GRL+ @ ICML 2020] PyTorch implementation for "Deep Graph Contrastive Representation Learning" (https://arxiv.org/abs/2006.04131v2)

Graph-MLP

awesome-graph-self-supervised-learning

Code for TKDE paper "Self-supervised learning on graphs: Contrastive, generative, or predictive"

awesome-self-supervised-learning-for-graphs

A curated list for awesome self-supervised learning for graphs.

pytorch_sparse

PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations

DL-miRNA-disease-association-methods

KnowledgeGraphEmbedding

KGCN-pytorch

KGCN pytorch model implementation

KGs-Survey

This is a repository of knowledge graphs survey paper that will be updated periodically.

go

The Go programming language

GNNs-Recipe

🟠 A study guide to learn about Graph Neural Networks (GNNs)

Survey_AI_Drug_Discovery

Machine-Learning-From-Scratch

常用机器学习的算法简洁实现

graph-based-deep-learning-literature

links to conference publications in graph-based deep learning

MGATE

MGATE model and supplementary data

Graph-Learning

Graph model implementation

NIMGSA

Predicting miRNA–disease association based on neural inductive matrix completion with graph autoencoders and self-attention mechanism

GATMDA

Predicting miRNA-disease associations based on graph attention network with multi-source information

AMMGC

pyGAT

Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)

GATECDA

This code and data were provided for the paper "Predicting CircRNA-Drug Sensitivity Associations via Graph Attention Auto-Encoder"

GATE

Graph Attention Auto-Encoders