Similarity-Dissimilarity Loss with Supervised Contrastive Learning for
Multi-label Classification
Similarity-Dissimilarity Loss with Supervised Contrastive Learning for
Multi-label Classification
Supervised contrastive learning has been explored in making use of label information for multi-label classification, but determining positive samples in multi-label scenario remains challenging. Previous studies have examined strategies for identifying positive samples, considering label overlap proportion between anchors and samples. However, they ignore various relations between given anchors and …