Seungjong Noh

Self-Contrastive Learning: Single-Viewed Supervised Contrastive Framework Using Sub-network

Contrastive loss has significantly improved performance in supervised classification tasks by using a multi-viewed framework that leverages augmentation and label information. The augmentation enables contrast with another view of a single image but enlarges training time and memory usage. To exploi…