Robust Domain Generalization under Divergent Marginal and Conditional Distributions

A unified meta-learning framework minimizes decomposed risk bounds across marginal and conditional distribution shifts to achieve robust domain generalization. It achieves state-of-the-art results on standard benchmarks and challenging multi-domain long-tailed recognition settings.

Jewon Yeom, Kyubyung Chae, Hyunggyu Lim, Yoonna Oh and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026