Active Test Time Adaptation

Active Test-Time Adaptation (ATTA) enhances machine learning models by selectively adapting them to unseen data distributions during inference, addressing the limitations of traditional test-time adaptation (TTA) which often struggles with significant distribution shifts. Current research focuses on integrating active learning strategies, allowing the model to request labels for specific, informative data points to guide adaptation, and on improving existing entropy-based methods through techniques like robust label assignment and sample selection. This approach promises significant improvements in model robustness and accuracy across diverse applications, particularly where labeled data is scarce or expensive to obtain during deployment.