Difficult Examples Hurt Unsupervised Contrastive Learning: A Theoretical Perspective

TL;DR

We introduce a similarity-based theoretical framework that shows how difficult boundary examples impair generalization in unsupervised contrastive learning, and we design mechanisms that address this issue and boost downstream accuracy.

Opening excerpt from the authors’ abstract. source