2 papers
cs.LG2025
A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning
Chungpa Lee, Jeongheon Oh, Kibok Lee +1
Supervised contrastive learning (SupCL) has emerged as a prominent approach in representation learning, leveraging both supervised and self-supervised losses. However, achieving an…
cs.AI2025
ARES: Auxiliary Range Expansion for Outlier Synthesis
Eui-Soo Jung, Hae-Hun Seo, Hyun-Woo Jung +2
Recent successes of artificial intelligence and deep learning often depend on the well-collected training dataset which is assumed to have an identical distribution with the test d…