Frequency-Aware Domain Generalization.

Deep Neural Networks (DNNs) exhibit surprising zero-shot generalization and emergent phenomena across various tasks. However, the underlying mechanisms behind these behaviors remain unclear. By analyzing the perception of image frequencies by DNNs, we establish the association between the generalization behavior and frequency-aware regions. DNNs with stronger generalization exhibit wider frequency-aware regions. Therefore, we improve the generalization performance by broadening the frequency awareness. Specifically, we enable DNNs to learn the relations between high-frequency components and semantic labels through frequency decomposition and mixup. Based on hierarchical feature alignment, we allow larger submodels to guide the frequency awareness of smaller submodels. Beyond training, we ensemble submodels to extract features from different frequency bands to enrich DNNs' frequency awareness during inference. We validate the effectiveness of our proposed method in image classification and object detection tasks in single and multi-source domain generalization scenarios. We also demonstrate the plug-and-play scalability of our method across existing approaches and different DNNs.