Machine learning methods strive to acquire a robust model during the training process that can effectively generalize to test samples, even in the presence of distribution shifts. However, these methods often suffer from performance degradation due to unknown test distributions. Test-time adaptation (TTA), an emerging paradigm, has the potential to adapt a pre-trained model to unlabeled data during testing, before making predictions. Recent progress in this paradigm has highlighted the significant benefits of using unlabeled data to train self-adapted models prior to inference. In this survey, we categorize TTA into several distinct groups based on the form of test data, namely, test-time domain adaptation, test-time batch adaptation, and online test-time adaptation. For each category, we provide a comprehensive taxonomy of advanced algorithms and discuss various learning scenarios. Furthermore, we analyze relevant applications of TTA and discuss open challenges and promising areas for future research. For a comprehensive list of TTA methods, kindly refer to \url{https://github.com/tim-learn/awesome-test-time-adaptation}.

翻译：机器学习方法致力于在训练过程中获得一个鲁棒的模型，即使存在分布偏移，该模型也能有效地泛化到测试样本。然而，由于未知的测试分布，这些方法常常遭受性能下降。测试时自适应作为一种新兴范式，有潜力在测试阶段对未标记数据进行预测之前，使预训练模型适应这些数据。该范式的最新进展凸显了在推理前使用未标记数据训练自适应模型的显著优势。在本综述中，我们根据测试数据的形式将TTA分为几个不同的组别，即测试时域自适应、测试时批次自适应和在线测试时自适应。对于每个类别，我们提供了先进算法的全面分类，并讨论了各种学习场景。此外，我们分析了TTA的相关应用，并讨论了未来研究的开放挑战和有前景的领域。有关TTA方法的完整列表，请参阅 \url{https://github.com/tim-learn/awesome-test-time-adaptation}。