Dg Tta

DG-TTA (Domain Generalization and Test-Time Adaptation) research focuses on improving the adaptability of pre-trained models to new, unseen data domains without requiring additional labeled data from those domains. Current efforts concentrate on developing algorithms and model architectures, such as bidirectional adapters and memory buffers, that enable effective model adaptation during the testing phase, often leveraging techniques like self-supervision and multi-modal learning. This work is significant for applications like medical image segmentation, remote physiological measurement, and autonomous driving, where retraining models for every new scenario is impractical or impossible due to data limitations or privacy concerns.