3 papers
cs.LG2025
On the Similarities of Embeddings in Contrastive Learning
Chungpa Lee, Sehee Lim, Kibok Lee +1
Contrastive learning operates on a simple yet effective principle: Embeddings of positive pairs are pulled together, while those of negative pairs are pushed apart. In this paper,…
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.LG2025
A Generalized Theory of Mixup for Structure-Preserving Synthetic Data
Chungpa Lee, Jongho Im, Joseph H. T. Kim
Mixup is a widely adopted data augmentation technique known for enhancing the generalization of machine learning models by interpolating between data points. Despite its success an…