2025
A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning
AISTATS 2025poster
Supervised contrastive learning (SupCL) has emerged as a prominent approach in representation learning, leveraging both supervised and self-supervised losses. However, achieving an optimal balance between these losses is challenging; failing to do so can lead to class collapse, reducing discriminati…