Progressive Invariant Causal Feature Learning for Single Domain Generalization

Single domain generalization (SDG) aims to transfer models trained on a single source domain to multiple unseen target domains while against the unknown domain shifts. The main challenge lies in learning the domain-invariant features to mitigate the domain shift impact. To address this challenge, we reconsider SDG from a causal perspective to capture the domain-invariant features accurately. Specifically, we present a Progressive Invariant Causal Feature Learning (PICF) method that leverages front-door adjustment to gradually obtain the invariant causal features for SDG. First, we introduce a foreground feature filter, which removes object-irrelevant confounders in a cyclical manner to extract the object-related causal features. Subsequently, to further enhance the causal feature invariance, we propose to train with augmented causal features by combining them with randomly-sampled styles from the object-irrelevant feature distribution boundary. As a result, our model bridges the gap between one seen domain and multiple unseen ones by capturing the invariant causal features, which largely enhances the model's generalization ability in SDG. In experiments, our method can be plugged into multiple state-of-the-art methods, and the significant performance improvements on multiple datasets demonstrate the superiority of our method. In particular, on the PACS dataset, our method achieves an accuracy improvement of 4.7%.