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Domain generalization (DG), aiming at models able to work on multiple unseendomains, is a must-have characteristic of general artificial intelligence. DGbased on single source domain training data is more challenging due to the lackof comparable information to help identify domain invariant features. In thispaper, it is determined that the domain invariant features could be containedin the single source domain training samples, then the task is to find properways to extract such domain invariant features from the single source domainsamples. An assumption is made that the domain invariant features are closelyrelated to the frequency. Then, a new method that learns through multiplefrequency domains is proposed. The key idea is, dividing the frequency domainof each original image into multiple subdomains, and learning features in thesubdomain by a designed two branches network. In this way, the model isenforced to learn features from more samples of the specifically limitedspectrum, which increases the possibility of obtaining the domain invariantfeatures that might have previously been defiladed by easily learned features.Extensive experimental investigation reveals that 1) frequency decompositioncan help the model learn features that are difficult to learn. 2) the proposedmethod outperforms the state-of-the-art methods of single-source domaingeneralization.

Frequency Decomposition to Tap the Potential of Single Domain for Generalization