Test Time Adaptation

Test-time Adaptation is a technique that fine-tunes a model during the testing phase, aiming to enable pre-trained models to adapt to new, unseen data distributions, thereby enhancing their robustness and generalization in practical application scenarios. This technology dynamically adjusts the model parameters by utilizing a small amount of labeled or unlabeled data at test time, ensuring that the model's performance remains stable when facing domain shifts. In the field of computer vision, Test-time Adaptation has significant application value for improving model performance under different environments and conditions.