Long-tail learning via logit adjustment

Code

• github.com/google-research/google-research/tree/master/logit_adjustment
  Officialtf★ 0
• github.com/Chumsy0725/logit-adj-pytorch
  pytorch★ 117
• github.com/hbzju/solar
  pytorch★ 30

Abstract

Real-world classification problems typically exhibit an imbalanced or long-tailed label distribution, wherein many labels are associated with only a few samples. This poses a challenge for generalisation on such labels, and also makes na\"ive learning biased towards dominant labels. In this paper, we present two simple modifications of standard softmax cross-entropy training to cope with these challenges. Our techniques revisit the classic idea of logit adjustment based on the label frequencies, either applied post-hoc to a trained model, or enforced in the loss during training. Such adjustment encourages a large relative margin between logits of rare versus dominant labels. These techniques unify and generalise several recent proposals in the literature, while possessing firmer statistical grounding and empirical performance.

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