PyTorch implementation for Graph Contrastive Learning with Augmentations

Overview

Graph Contrastive Learning with Augmentations

PyTorch implementation for Graph Contrastive Learning with Augmentations [poster] [appendix]

Yuning You*, Tianlong Chen*, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen

In NeurIPS 2020.

Overview

In this repository, we develop contrastive learning with augmentations for GNN pre-training (GraphCL, Figure 1) to address the challenge of data heterogeneity in graphs. Systematic study is performed as shown in Figure 2, to assess the performance of contrasting different augmentations on various types of datasets.

Experiments

Citation

If you use this code for you research, please cite our paper.

@inproceedings{You2020GraphCL,
 author = {You, Yuning and Chen, Tianlong and Sui, Yongduo and Chen, Ting and Wang, Zhangyang and Shen, Yang},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
 pages = {5812--5823},
 publisher = {Curran Associates, Inc.},
 title = {Graph Contrastive Learning with Augmentations},
 url = {https://proceedings.neurips.cc/paper/2020/file/3fe230348e9a12c13120749e3f9fa4cd-Paper.pdf},
 volume = {33},
 year = {2020}
}
Owner
Shen Lab at Texas A&M University
Shen Lab at Texas A&M University
Code for 'Self-Guided and Cross-Guided Learning for Few-shot segmentation. (CVPR' 2021)'

SCL Introduction Code for 'Self-Guided and Cross-Guided Learning for Few-shot segmentation. (CVPR' 2021)' We evaluated our approach using two baseline

34 Oct 08, 2022
U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection

The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."

Xuebin Qin 6.5k Jan 09, 2023
Official implementation of "Membership Inference Attacks Against Self-supervised Speech Models"

Introduction Official implementation of "Membership Inference Attacks Against Self-supervised Speech Models". In this work, we demonstrate that existi

Wei-Cheng Tseng 7 Nov 01, 2022
An AutoML Library made with Optuna and PyTorch Lightning

An AutoML Library made with Optuna and PyTorch Lightning Installation Recommended pip install -U gradsflow From source pip install git+https://github.

GradsFlow 294 Dec 17, 2022
High frequency AI based algorithmic trading module.

Flow Flow is a high frequency algorithmic trading module that uses machine learning to self regulate and self optimize for maximum return. The current

59 Dec 14, 2022
RAMA: Rapid algorithm for multicut problem

RAMA: Rapid algorithm for multicut problem Solves multicut (correlation clustering) problems orders of magnitude faster than CPU based solvers without

Paul Swoboda 60 Dec 13, 2022
Self-supervised learning algorithms provide a way to train Deep Neural Networks in an unsupervised way using contrastive losses

Self-supervised learning Self-supervised learning algorithms provide a way to train Deep Neural Networks in an unsupervised way using contrastive loss

Arijit Das 2 Mar 26, 2022
use tensorflow 2.0 to tell a dog and cat from a specified picture

dog_or_cat use tensorflow 2.0 to tell a dog and cat from a specified picture This is one of the classic experiments for the introduction of deep learn

你这个代码我看不懂 1 Oct 22, 2021
Playable Video Generation

Playable Video Generation Playable Video Generation Willi Menapace, Stéphane Lathuilière, Sergey Tulyakov, Aliaksandr Siarohin, Elisa Ricci Paper: ArX

Willi Menapace 136 Dec 31, 2022
Scheme for training and applying a label propagation framework

Factorisation-based Image Labelling Overview This is a scheme for training and applying the factorisation-based image labelling (FIL) framework. Some

Wellcome Centre for Human Neuroimaging 2 Dec 17, 2021
最新版本yolov5+deepsort目标检测和追踪,支持5.0版本可训练自己数据集

使用YOLOv5+Deepsort实现车辆行人追踪和计数,代码封装成一个Detector类,更容易嵌入到自己的项目中。

422 Dec 30, 2022
A PyTorch implementation of the Relational Graph Convolutional Network (RGCN).

Torch-RGCN Torch-RGCN is a PyTorch implementation of the RGCN, originally proposed by Schlichtkrull et al. in Modeling Relational Data with Graph Conv

Thiviyan Singam 66 Nov 30, 2022
"Learning and Analyzing Generation Order for Undirected Sequence Models" in Findings of EMNLP, 2021

undirected-generation-dev This repo contains the source code of the models described in the following paper "Learning and Analyzing Generation Order f

Yichen Jiang 0 Mar 25, 2022
[CVPR 2021] Generative Hierarchical Features from Synthesizing Images

[CVPR 2021] Generative Hierarchical Features from Synthesizing Images

GenForce: May Generative Force Be with You 148 Dec 09, 2022
ICLR2021 (Under Review)

Self-Supervised Time Series Representation Learning by Inter-Intra Relational Reasoning This repository contains the official PyTorch implementation o

Haoyi Fan 58 Dec 30, 2022
A Partition Filter Network for Joint Entity and Relation Extraction EMNLP 2021

EMNLP 2021 - A Partition Filter Network for Joint Entity and Relation Extraction

zhy 127 Jan 04, 2023
Official PyTorch implementation of "Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets" (ICLR 2021)

Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets This is the official PyTorch implementation for the paper Rapid Neural A

48 Dec 26, 2022
This code finds bounding box of a single human mouth.

This code finds bounding box of a single human mouth. In comparison to other face segmentation methods, it is relatively insusceptible to open mouth conditions, e.g., yawning, surgical robots, etc. T

iThermAI 4 Nov 27, 2022
The repo for the paper "I3CL: Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection".

I3CL: Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection Updates | Introduction | Results | Usage | Citation |

33 Jan 05, 2023