Domain generalization aims to develop a model that can perform well on unseen target domains by learning from multiple source domains. However, recent-proposed domain generalization models usually rely on domain labels, which may not be available in many real-world scenarios. To address this challenge, we propose a Discriminant Risk Minimization (DRM) theory and the corresponding algorithm to capture the invariant features without domain labels. In DRM theory, we prove that reducing the discrepancy of prediction distribution between overall source domain and any subset of it can contribute to obtaining invariant features. To apply the DRM theory, we develop an algorithm which is composed of Bayesian inference and a new penalty termed as Categorical Discriminant Risk (CDR). In Bayesian inference, we transform the output of the model into a probability distribution to align with our theoretical assumptions. We adopt sliding update approach to approximate the overall prediction distribution of the model, which enables us to obtain CDR penalty. We also indicate the effectiveness of these components in finding invariant features. We evaluate our algorithm against various domain generalization methods on multiple real-world datasets, providing empirical support for our theory.

通过基于判别风险最小化理论和算法的不变特征捕获来解决领域泛化中没有领域标签的挑战，通过测试真实数据集验证了该方法的有效性。

领域无关条件不变预测