Domain Shift Problem

The domain shift problem arises when machine learning models trained on one data distribution (source domain) perform poorly on data from a different distribution (target domain). Current research focuses on mitigating this issue through techniques like test-time adaptation (TTA), which adjusts models to target domains using unlabeled data, and domain generalization (DG), which aims to build models robust across unseen domains. These efforts leverage various approaches, including diffusion models, contrastive learning, and adversarial training, with applications spanning diverse fields such as medical image analysis, autonomous driving, and speech recognition, ultimately improving the reliability and generalizability of AI systems.