We present a novel approach for test-time adaptation via online self-training, consisting of two components. First, we introduce a statistical framework that detects distribution shifts in the classifier's entropy values obtained on a stream of unlabeled samples. Second, we devise an online adaptation mechanism that utilizes the evidence of distribution shifts captured by the detection tool to dynamically update the classifier's parameters. The resulting adaptation process drives the distribution of test entropy values obtained from the self-trained classifier to match those of the source domain, building invariance to distribution shifts. This approach departs from the conventional self-training method, which focuses on minimizing the classifier's entropy. Our approach combines concepts in betting martingales and online learning to form a detection tool capable of quickly reacting to distribution shifts. We then reveal a tight relation between our adaptation scheme and optimal transport, which forms the basis of our novel self-supervised loss. Experimental results demonstrate that our approach improves test-time accuracy under distribution shifts while maintaining accuracy and calibration in their absence, outperforming leading entropy minimization methods across various scenarios.

本研究针对测试时适应中的分布转变问题，提出了一种创新的方法，其核心在于建立一个统计框架来检测分类器熵值的分布转变，并通过在线适应机制动态更新分类器参数。实验结果表明，该方法在面对分布转变时显著提高了测试时的准确性，同时在没有分布转变时也保持了准确性和校准性能，优于传统的熵最小化方法。

通过在线熵匹配保护测试时适应：一种投注方法