This paper considers the out-of-distribution (OOD) generalization problem under the setting that both style distribution shift and spurious features exist and domain labels are missing. This setting frequently arises in real-world applications and is underlooked because previous approaches mainly handle either of these two factors. The critical challenge is decoupling style and spurious features in the absence of domain labels. To address this challenge, we first propose a structural causal model (SCM) for the image generation process, which captures both style distribution shift and spurious features. The proposed SCM enables us to design a new framework called IRSS, which can gradually separate style distribution and spurious features from images by introducing adversarial neural networks and multi-environment optimization, thus achieving OOD generalization. Moreover, it does not require additional supervision (e.g., domain labels) other than the images and their corresponding labels. Experiments on benchmark datasets demonstrate that IRSS outperforms traditional OOD methods and solves the problem of Invariant risk minimization (IRM) degradation, enabling the extraction of invariant features under distribution shift.

翻译：本文考虑在风格分布偏移和虚假特征同时存在且域标签缺失的场景下的分布外（OOD）泛化问题。该场景在现实应用中频繁出现，但由于先前方法主要处理上述两个因素之一，因而长期被忽视。关键挑战在于如何在缺乏域标签的情况下解耦风格与虚假特征。为应对这一挑战，我们首先为图像生成过程提出结构因果模型（SCM），该模型同时捕捉风格分布偏移和虚假特征。所提出的SCM使我们能够设计名为IRSS的新框架，通过引入对抗神经网络和多环境优化，逐步从图像中分离风格分布与虚假特征，从而实现OOD泛化。此外，该方法除图像及其对应标签外无需额外监督信息（如域标签）。在基准数据集上的实验表明，IRSS优于传统OOD方法，并解决了不变风险最小化（IRM）退化问题，能够提取分布偏移下的不变特征。