  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.
