Domain Invariance

Domain invariance in machine learning focuses on developing models robust to variations in data distribution across different domains, enabling knowledge transfer and generalization to unseen data. Current research explores various techniques, including learning domain-invariant representations through methods like risk distribution matching and contrastive learning, as well as approaches that condition representations on target variables or leverage auxiliary tasks like prototype alignment. These advancements are crucial for improving the reliability and generalizability of machine learning models across diverse real-world applications, particularly in areas like medical imaging and natural language processing where data heterogeneity is common.