频率分解以挖掘单域潜力实现泛化
Frequency Decomposition to Tap the Potential of Single Domain for Generalization
AI总结：
本文提出一种基于频率分解的单源域泛化方法，通过将图像频域划分为多个子域并用双分支网络学习，迫使模型挖掘难以学习但域不变的特征，实验证明其优于现有方法。
摘要：
域泛化（DG）旨在使模型能够在多个未见域上工作，是通用人工智能必须具备的特性。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。本文确定域不变特征可能包含在单源域训练样本中，因此任务在于寻找合适的方法从单源域样本中提取此类域不变特征。本文假设域不变特征与频率密切相关。然后，提出了一种通过多个频率域进行学习的新方法。其核心思想是，将每个原始图像的频率域划分为多个子域，并通过设计的两分支网络在子域中学习特征。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，从而增加了获得域不变特征的可能性，这些特征此前可能被容易学习的特征所掩盖。大量实验研究表明：1）频率分解可以帮助模型学习难以学习的特征；2）所提出的方法优于当前最先进的单源域泛化方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
