Yong Jiang

Recurrent Contrastive Learning for Imbalanced Medical Image Classification

Recurrent Contrastive Learning progressively expands the support region of tail classes by recurrently reusing historical feature states across training phases and devise a Temporal Memory Queue (TMQ) to preserve corpus-level features across training phases and provide diversified global references for contrastive learning.