Domain generalization (DG), aiming at models able to work on multiple unseen domains, is a must-have characteristic of general artificial intelligence. DG based on single source domain training data is more challenging due to the lack of comparable information to help identify domain invariant features. In this paper, it is determined that the domain invariant features could be contained in the single source domain training samples, then the task is to find proper ways to extract such domain invariant features from the single source domain samples. An assumption is made that the domain invariant features are closely related to the frequency. Then, a new method that learns through multiple frequency domains is proposed. The key idea is, dividing the frequency domain of each original image into multiple subdomains, and learning features in the subdomain by a designed two branches network. In this way, the model is enforced to learn features from more samples of the specifically limited spectrum, which increases the possibility of obtaining the domain invariant features that might have previously been defiladed by easily learned features. Extensive experimental investigation reveals that 1) frequency decomposition can help the model learn features that are difficult to learn. 2) the proposed method outperforms the state-of-the-art methods of single-source domain generalization.

翻译：域泛化（DG）旨在使模型能够在多个未见域上工作，是通用人工智能的必备特性。基于单源域训练数据的域泛化更具挑战性，因为缺乏可比较的信息来帮助识别域不变特征。本文证实，域不变特征可能蕴含在单源域训练样本中，因此任务在于寻找适当的方法从单源域样本中提取此类域不变特征。假设域不变特征与频率密切相关，进而提出一种通过多频率域进行学习的新方法。其核心思想是：将每张原始图像的频域划分为多个子域，并通过设计的两分支网络学习子域中的特征。通过这种方式，模型被迫从特定受限频谱的更多样本中学习特征，从而增加了获得那些可能被易学特征掩盖的域不变特征的可能性。大量实验研究表明：1）频率分解有助于模型学习难以学习的特征；2）所提方法性能优于当前最优的单源域泛化方法。