【摘要】 域泛化是计算机视觉领域中一个重要且具有挑战性的问题，该问题源于现实场景中的数据分布偏移。在实际应用中，通常会遇到训练数据和测试数据来自不同的数据域的情况，这种数据分布的差异会导致测试时准确率下降。因此，提出了一种基于隐空间数据增广的域泛化方法，与传统图像级数据增广方法不同，该方法在隐空间中引入扩散模型，以实现对特征的精细控制和多样性生成，从而提升模型在目标域上的泛化能力。具体来说，基于分类器的隐式扩散模型在隐空间训练后可以条件生成准确且丰富的源域特征，并利用高效的采样方法加速生成增广特征。实验结果表明，新提出的方法在各种域泛化任务上取得了显著的性能提升，在真实场景中有较好的有效性和鲁棒性。该方法的创新点在于将数据增广焦点转移到隐空间级别，并引入扩散模型进行增广，为解决域泛化问题提供了一种新的思路。更多还原

【Abstract】 Domain generalization is an important and challenging problem in computer vision, arising from the distribution shift of real-world data. In practical applications, it is common to encounter training and testing data from different domains, and the difference in data distribution can lead to performance degradation during testing. In this paper, we propose a domain generalization method based on latent space data augmentation. Unlike traditional image-level data augmentation approaches, the method introduces a diffusion model in the latent space to achieve fine control and diversity generation of features, thereby achieving feature level data augmentation and enhancing the model’s generalization ability in the target domain. Specifically, the classifier-based implicit diffusion model, trained within the latent space, can conditionally generate accurate and rich source domain features. It leverages efficient sampling techniques to expedite the generation of augmented features. Experimental results show that the method has achieved significant performance improvement in various domain generalization tasks, and has good effectiveness and robustness in real scenarios. The key innovation of this paper lies in shifting data augmentation to the latent space level and introducing the diffusion model for augmentation, providing a novel approach to address the domain generalization problem.更多还原