Test Time Domain Adaptation

Test-time domain adaptation (TTDA) focuses on adapting pre-trained models to new, unseen data distributions encountered during inference, without access to the original training data. Current research emphasizes techniques like meta-learning, adversarial training, and optimal transport to improve model robustness and generalization across domains, often targeting specific architectures such as transformers and convolutional neural networks. These methods are crucial for deploying machine learning models in real-world scenarios where data distributions inevitably shift, improving the reliability and performance of applications ranging from medical image analysis to autonomous driving. The field is actively exploring efficient adaptation strategies, including those that leverage limited labeled or unlabeled target data and incorporate human-in-the-loop approaches.