SimO Loss: Anchor-Free Contrastive Loss for Fine-Grained Supervised Contrastive Learning

Taha Bouhsine, Imad El Aaroussi, Atik Faysal +1

We introduce a novel anchor-free contrastive learning (AFCL) method leveraging our proposed Similarity-Orthogonality (SimO) loss. Our approach minimizes a semi-metric discriminativ…