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Where Does the Semantic Gain Come From? A Reproduction and Extension of Semantic Knowledge-driven Contrastive Learning for Long-Tailed Recognition
Semantic Knowledge-driven Contrastive Learning (SKCL) uses a language model to decide which classes are related, and pulls each image towards the prototypes of its semantic neighbours. On CIFAR-100-LT (beta = 100) it reports 54.02% top-1 accuracy, 2.01 points above Balanced Contrastive Learning (BCL
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