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Machine learning algorithms minimizing the average training loss usuallysuffer from poor generalization performance due to the greedy exploitation ofcorrelations among the training data, which are not stable under distributionalshifts. It inspires various works for domain generalization (DG), where aseries of methods, such as Causal Matching and FISH, work by pairwise domainoperations. They would need $O(n^2)$ pairwise domain operations with $n$domains, where each one is often highly expensive. Moreover, while a commonobjective in the DG literature is to learn invariant representations againstdomain-induced spurious correlations, we highlight the importance of mitigatingspurious correlations caused by objects. Based on the observation thatdiversity helps mitigate spurious correlations, we propose a Diversity boostedtwO-level saMplIng framework (DOMI) utilizing Determinantal Point Processes(DPPs) to efficiently sample the most informative ones among large number ofdomains. We show that DOMI helps train robust models against spuriouscorrelations from both domain-side and object-side, substantially enhancing theperformance of the backbone DG algorithms on rotated MNIST, rotated FashionMNIST, and iwildcam datasets.

Diversity Boosted Learning for Domain Generalization with Large Number of Domains