Graph Contrastive Learning Automated PyTorch implementation for Graph Contrastive Learning Automated . Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang In ICML 2021. Overview In this repository, we propose a principled framework named joint augmentation selection (JOAO), to automatically, adaptively and dynamically select augmentations during GraphCL training.Sanity check shows that the selection aligns with previous “best practices”, as shown in Figure 2. Dependencies Experiments Citation If you use this code for you research, please cite our paper. @article{you2021graph, title={Graph Contrastive […]

SDCLR The recent breakthrough achieved by contrastive learning accelerates the pace for deploying unsupervised training on real-world data applications. However, unlabeled data in reality is commonly imbalanced and shows a long-tail distribution, and it is unclear how robustly the latest contrastive learning methods could perform in the practical scenario. This paper proposes to explicitly tackle this challenge, via a principled framework called Self-Damaging Contrastive Learning (SDCLR), to automatically balance the representation learning without knowing the classes. Our main inspiration is […]

Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting, CVPR 2021. Ayan Kumar Bhunia, Pinaki nath Chowdhury, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song, “Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting”, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021. Abstract Self-supervised learning has gained prominence due to its efficacy at learning powerful representations from unlabelled data that achieve excellent performance on many challenging downstream tasks. However, supervision-free pre-text tasks are challenging to design and usually […]

PS-SC GAN This repository contains the main code for training a PS-SC GAN (a GAN implemented with the Perceptual Simplicity and Spatial Constriction constraints) introduced in the paper Where and What? Examining Interpretable Disentangled Representations. The code for computing the TPL for model checkpoints from disentanglemen_lib can be found in this repository. Abstract Capturing interpretable variations has long been one of the goals indisentanglement learning. However, unlike the independence assumption,interpretability has rarely been exploited to encourage disentanglementin the unsupervised setting. […]