2 papers
cs.LG2025
Understanding Self-supervised Contrastive Learning through Supervised Objectives
Byeongchan Lee
Self-supervised representation learning has achieved impressive empirical success, yet its theoretical understanding remains limited. In this work, we provide a theoretical perspec…
cs.LG2025
Implicit Contrastive Representation Learning with Guided Stop-gradient
Byeongchan Lee, Sehyun Lee
In self-supervised representation learning, Siamese networks are a natural architecture for learning transformation-invariance by bringing representations of positive pairs closer…