COME: Test-time adaption by Conservatively Minimizing Entropy

• URL: http://arxiv.org/abs/2410.10894v1
• Date: Sat, 12 Oct 2024 09:20:06 GMT
• Title: COME: Test-time adaption by Conservatively Minimizing Entropy
• Authors: Qingyang Zhang, Yatao Bian, Xinke Kong, Peilin Zhao, Changqing Zhang,
• Abstract summary: Conservatively Minimize the Entropy (COME) is a drop-in replacement of traditional entropy (EM) COME explicitly models the uncertainty by characterizing a Dirichlet prior distribution over model predictions. We show that COME achieves state-of-the-art performance on commonly used benchmarks.
• Score: 45.689829178140634
• License:
• 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, i.e., up to $34.5\%$ improvement on accuracy and $15.1\%$ on false positive rate.

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