Test Time

Test-time adaptation focuses on improving the performance of machine learning models during inference, without requiring retraining, by leveraging information from the test data itself. Current research emphasizes techniques like adjusting normalization statistics, employing self-training strategies, and integrating additional information such as text prompts or user-provided knowledge to enhance model robustness and accuracy in the face of distribution shifts or noisy data. This area is crucial for deploying models in real-world scenarios where retraining is impractical or impossible, impacting fields ranging from medical image analysis to natural language processing and robotics. The development of efficient and effective test-time adaptation methods is vital for improving the reliability and generalizability of machine learning systems.