Learning Label Shift Correction for Test-Agnostic Long-Tailed Recognition

Tong Wei, Zhen Mao, Zi-Hao Zhou, Yuanyu Wan, Min-Ling Zhang

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Keywords: long-tail learning, label shift, semi-supervised learning

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TL;DR: This paper proposes a simple yet effective approach for tackling test label shifts in long-tail learning.

Abstract: Long-tail learning primarily focuses on mitigating the label distribution shift between long-tailed training data and uniformly distributed test data. However, in real-world applications, we often encounter a more intricate challenge where the test label distribution is agnostic. To address this problem, we first theoretically establish the substantial potential for reducing generalization error if we can precisely estimate the test label distribution. Motivated by the theoretical insight, we introduce a simple yet effective solution called label shift correction (LSC). LSC estimates the test label distribution within the proposed framework of generalized black box shift estimation, and adjusts the model predictions to align with the estimated distribution. Theoretical analyses confirm that accurate test label distribution estimates can effectively reduce the generalization error. Extensive experimental results demonstrate that our method significantly outperforms previous state-of-the-art approaches, especially when confronted with non-uniform test label distribution. Notably, the proposed method is general and complements existing long-tail learning approaches, consistently improving their performance.

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Submission Number: 1806