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.
Domain-invariant Feature Exploration for Domain Generalization
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Microsoft Research Asia（微软亚洲研究院）；City University of Hong Kong（香港城市大学）；Peng Cheng Laboratory（鹏城实验室）
Revisiting Invariant Learning for Out-of-Domain Generalization on Multi-Site Mammogram Datasets
Department of Electrical and Computer Engineering, University of Houston（电气与计算机工程系，休斯顿大学）；Department of Systems Medicine and Biomedical Engineering, Houston Methodist Cancer Center（系统医学与生物医学工程系，休斯顿方法主义癌症中心）
Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization
Sydney AI Centre, The University of Sydney（悉尼大学悉尼人工智能中心）；JD Explore Academy（京东探索研究院）；University of Science and Technology of China（中国科学技术大学）；Nanjing University of Science and Technology（南京理工大学）；Hong Kong Baptist University（香港浸会大学）
Towards a Universal 3D Medical Multi-modality Generalization via Learning Personalized Invariant Representation
Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）；Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学）；University of Liverpool（利物浦大学）；AI 3 Fudan University（复旦大学）；Duke Kunshan University（杜克-昆山大学）；Zhongshan Hospital（中山医院）
Domain Generalization via Conditional Invariant Representation
University of Science and Technology of China（中国科学技术大学）；The University of Sydney（悉尼大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh（匹兹堡大学）
Class-Invariant Test-Time Augmentation for Domain Generalization
School of Computing and Artificial Intelligence（计算机与人工智能学院）；ANGEL Lab（ANGEL实验室）；Southwest Jiaotong University（西南交通大学）；Nanyang Technological University（南洋理工大学）
