DICS: Find Domain-Invariant and Class-Specific Features for Out-of-Distribution Generalization
<|reference_start|>DICS: Find Domain-Invariant and Class-Specific Features for Out-of-Distribution Generalization: While deep neural networks have made remarkable progress in various vision tasks, their performance typically deteriorates when tested in out-of-distribution (OOD) scenarios. Many OOD methods focus on extracting domain-invariant features but neglect whether these features are unique to each class. Even if some features are domain-invariant, they cannot serve as key classification criteria if shared across different classes. In OOD tasks, both domain-related and class-shared features act as confounders that hinder generalization. In this paper, we propose a DICS model to extract Domain-Invariant and Class-Specific features, including Domain Invariance Testing (DIT) and Class Specificity Testing (CST), which mitigate the effects of spurious correlations introduced by confounders. DIT learns domain-related features of each source domain and removes them from inputs to isolate domain-invariant class-related features. DIT ensures domain invariance by aligning same-class features across different domains. Then, CST calculates soft labels for those features by comparing them with features learned in previous steps. We optimize the cross-entropy between the soft labels and their true labels, which enhances same-class similarity and different-class distinctiveness, thereby reinforcing class specificity. Extensive experiments on widely-used benchmarks demonstrate the effectiveness of our proposed algorithm. Additional visualizations further demonstrate that DICS effectively identifies the key features of each class in target domains.<|reference_end|>
@article{miao2024dics:,
    title={DICS: Find Domain-Invariant and Class-Specific Features for
  Out-of-Distribution Generalization},
    author={Qiaowei Miao, Yawei Luo, Yi Yang},
    journal={arXiv preprint arXiv:2409.08557},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08557},
    primaryClass={cs.CV}
}