ACL modifies supervised contrastive loss by removing non-effective positives from the denominator, adding class centers, and re-weighting negatives by inverse class frequency, yielding new state-of-the-art accuracy on four long-tailed benchmarks.
Big self-supervised mod- els are strong semi-supervised learners.Advances in neural information processing systems, 33:22243–22255, 2020
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Aligned Contrastive Loss for Long-Tailed Recognition
ACL modifies supervised contrastive loss by removing non-effective positives from the denominator, adding class centers, and re-weighting negatives by inverse class frequency, yielding new state-of-the-art accuracy on four long-tailed benchmarks.