COME: Test-time Adaption by Conservatively Minimizing Entropy

ICLR 2025 Conference Submission632 Authors

Keywords: Test-time adaption, Out-of-distribution generalization

TL;DR: We propose an alternative to entropy minimization as a better learning principle for TTA tasks.

Abstract: Machine learning models must continuously self-adjust themselves for novel data distribution in the open world. As the predominant principle, entropy minimization (EM) has been proven to be a simple yet effective cornerstone in existing test-time adaption (TTA) methods. While unfortunately its fatal limitation (i.e., overconfidence) tends to result in model collapse. For this issue, we propose to conservatively minimize the entropy (COME), which is a simple drop-in replacement of traditional EM to elegantly address the limitation. In essence, COME explicitly models the uncertainty by characterizing a Dirichlet prior distribution over model predictions during TTA. By doing so, COME naturally regularizes the model to favor conservative confidence on unreliable samples. Theoretically, we provide a preliminary analysis to reveal the ability of COME in enhancing the optimization stability by introducing a data-adaptive lower bound on the entropy. Empirically, our method achieves state-of-the-art performance on commonly used benchmarks, showing significant improvements in terms of classification accuracy and uncertainty estimation under various settings including standard, life-long and open-world TTA. Our code is available at: \href{https://anonymous.4open.science/r/anonymous-9F46}{https://anonymous.4open.science/r/anonymous-9F46}.

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

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Submission Number: 632