Learning Class and Domain Augmentations for Single-Source Open-Domain Generalization

Developed SODG-Net, an end-to-end network outperforming benchmarks by 1-14% in open-domain settings. Proposed a style synthesis block generating diverse statistical features to effectively simulate novel domains. Formulated novel weight learning and margin objectives to establish distinct representations for open classes.