A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts

Abstract

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}.

Discussions, comments, and questions are all welcomed in \url{https://github.com/tim-learn/awesome-test-time-adaptation}

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Cited by in corpus (6)

• Enhancing and Adapting in the Clinic: Source-free Unsupervised Domain Adaptation for Medical Image Enhancement
• T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs
• Multi-Stream Cellular Test-Time Adaptation of Real-Time Models Evolving in Dynamic Environments
• Analyzing Effects of Mixed Sample Data Augmentation on Model Interpretability
• Predict the Retrieval! Test time adaptation for Retrieval Augmented Generation
• Data-Efficient Stream-Based Active Distillation for Scalable Edge Model Deployment