A Simplified Framework for Contrastive Learning for Node Representations
A Simplified Framework for Contrastive Learning for Node Representations
Contrastive learning has recently established itself as a powerful self-supervised learning framework for extracting rich and versatile data representations. Broadly speaking, contrastive learning relies on a data augmentation scheme to generate two versions of the input data and learns low-dimensional representations by optimizing the contrastive loss to identify augmented samples …