Divergence-Based Similarity Function for Multi-View Contrastive Learning

A new arXiv paper introduces a similarity measure built on divergence for contrastive learning with multiple augmented views. The authors note that earlier approaches combine views either in the loss or in the feature space, but largely restrict themselves to pairwise comparisons. Their method aims to capture relationships spanning more than two views at once. The work is labeled a cross-listing replacement on arXiv's machine learning section.