Pattern recognition (psychology)

Invariant (physics)

Domain adaptation

Mathematics

Domain (mathematical analysis)

Mathematical physics

Algorithm

Mathematical analysis

Surgery

Artificial intelligence

Randomization

Randomized controlled trial

Frequency domain

Computer science

Computer vision

Medicine

Segmentation

Classifier (UML)

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 methods use 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.

FSDR: Frequency Space Domain Randomization for Domain Generalization