Domain Divergence

Domain divergence, the difference in data distributions between different domains (e.g., datasets or environments), hinders the generalization ability of machine learning models. Current research focuses on mitigating this divergence through various techniques, including adversarial training, contrastive learning, and the use of large vision-language models like CLIP to measure and reduce domain discrepancies. These methods aim to learn domain-invariant features or calibrate predictions to improve model performance across diverse datasets, impacting fields like image recognition, natural language processing, and medical image analysis by enabling more robust and generalizable AI systems.