What is test-time adaptation in vision models?

Test-time adaptation updates model parameters during inference to handle distribution shifts.

In real-world scenarios, data distribution can shift between training and deployment. Test-time adaptation fine-tunes model parameters (often normalization layers) using unlabeled test data. This helps maintain performance under domain shift without retraining the full model.