Continuous Invariance Learning: Extracting Stable Features Across Continuously Indexed Domains

Existing invariance learning methods struggle to extract stable features across continuously indexed domains due to the limited samples per domain. Continuous Invariance Learning (CIL) addresses this challenge by aligning the conditional distribution of domain indices given the extracted features across different classes, enabling effective extraction of invariant features.