The paper derives a constant-additional-risk condition for label-noise robustness of any contrastive loss and proposes SymNCE, an InfoNCE variant that meets this condition in the infinite-sample limit.
Supervised contrastive learning,
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An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise
The paper derives a constant-additional-risk condition for label-noise robustness of any contrastive loss and proposes SymNCE, an InfoNCE variant that meets this condition in the infinite-sample limit.