Continual Test Time Adaptation

Continual Test-Time Adaptation (CTTA) focuses on adapting pre-trained models to continuously changing, unlabeled data streams during deployment, aiming to improve model robustness and adaptability in real-world scenarios. Current research emphasizes efficient adaptation strategies, often employing techniques like self-training with pseudo-labels, meta-learning, and selective parameter updates within architectures such as Mean Teacher models or by using visual prompts. This field is crucial for deploying machine learning models in dynamic environments, particularly in applications like autonomous driving and medical image analysis, where data distributions shift over time.