Domain generalization refers to the problem where we aim to train a model on data from a set of source domains so that the model can generalize to unseen target domains. Naively training a model on the aggregate set of data (pooled from all source domains) has been shown to perform suboptimally, since the information learned by that model might be domain-specific and generalize imperfectly to target domains. To tackle this problem, a predominant approach is to find and learn some domain-invariant information in order to use it for the prediction task. In this paper, we propose a theoretically grounded method to learn a domain-invariant representation by enforcing the representation network to be invariant under all transformation functions among domains. We also show how to use generative adversarial networks to learn such domain transformations to implement our method in practice. We demonstrate the effectiveness of our method on several widely used datasets for the domain generalization problem, on all of which we achieve competitive results with state-of-the-art models.
Robustness to Transformations Across Categories: Is Robustness To Transformations Driven by Invariant Neural Representations?
Massachusetts Institute of Technology（麻省理工学院）；Technical University of Munich（慕尼黑工业大学）；Center for Brains, Minds and Machines（大脑、心智与机器中心）；Fujitsu Research of America（富士通美国研究院）
Out-of-distribution Generalization with Causal Invariant Transformations
University of Chinese Academy of Sciences（中国科学院大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
Hierarchical and Permutation-Invariant Feature Transformation Learning via Policy-Guided Embedding Search
University of Kansas（堪萨斯大学）；Southwest University of Finance and Economics（西南财经大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；University of Auckland（奥克兰大学）；Arizona State University（亚利桑那州立大学）；Northeast Normal University（东北师范大学）
