  Domain generalization aims to learn a generalizable model from a known source
domain for various unknown target domains. It has been studied widely by domain
randomization that transfers source images to different styles in spatial space
for learning domain-agnostic features. However, most existing randomization
uses GANs that often lack of controls and even alter semantic structures of
images undesirably. Inspired by the idea of JPEG that converts spatial images
into multiple frequency components (FCs), we propose Frequency Space Domain
Randomization (FSDR) that randomizes images in frequency space by keeping
domain-invariant FCs (DIFs) and randomizing domain-variant FCs (DVFs) only.
FSDR has two unique features: 1) it decomposes images into DIFs and DVFs which
allows explicit access and manipulation of them and more controllable
randomization; 2) it has minimal effects on semantic structures of images and
domain-invariant features. We examined domain variance and invariance property
of FCs statistically and designed a network that can identify and fuse DIFs and
DVFs dynamically through iterative learning. Extensive experiments over
multiple domain generalizable segmentation tasks show that FSDR achieves
superior segmentation and its performance is even on par with domain adaptation
methods that access target data in training.
