Domain generalization (DG) aims to learn a robust model from source domains that generalize well on unseen target domains. Recent studies focus on generating novel domain samples or features to diversify distributions complementary to source domains. Yet, these approaches can hardly deal with the restriction that the samples synthesized from various domains can cause semantic distortion. In this paper, we propose an online one-stage Cross Contrasting Feature Perturbation (CCFP) framework to simulate domain shift by generating perturbed features in the latent space while regularizing the model prediction against domain shift. Different from the previous fixed synthesizing strategy, we design modules with learnable feature perturbations and semantic consistency constraints. In contrast to prior work, our method does not use any generative-based models or domain labels. We conduct extensive experiments on a standard DomainBed benchmark with a strict evaluation protocol for a fair comparison. Comprehensive experiments show that our method outperforms the previous state-of-the-art, and quantitative analyses illustrate that our approach can alleviate the domain shift problem in out-of-distribution (OOD) scenarios.

翻译：域泛化（Domain Generalization, DG）旨在从源域中学习鲁棒模型，使其能够良好泛化至未见的目标域。近期研究侧重于生成新型域的样本或特征，以扩展与源域互补的分布多样性。然而，这些方法难以应对从不同域合成的样本可能导致语义失真的限制。本文提出一种在线单阶段的跨对比特征扰动（Cross Contrasting Feature Perturbation, CCFP）框架，通过在潜在空间中生成扰动特征来模拟域偏移，同时正则化模型预测以抵抗域偏移。与先前固定的合成策略不同，我们设计了具有可学习特征扰动和语义一致性约束的模块。相较于以往工作，我们的方法不使用任何基于生成的模型或域标签。我们在标准DomainBed基准上进行了广泛实验，并采用严格的评估协议以确保公平对比。综合实验表明，我们的方法超越了先前的最优水平，定量分析进一步说明该方法能够缓解分布外（Out-of-Distribution, OOD）场景中的域偏移问题。