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Domain generalization aims to learn a generalization model that can performwell on unseen test domains by only training on limited source domains.However, existing domain generalization approaches often bring inprediction-irrelevant noise or require the collection of domain labels. Toaddress these challenges, we consider the domain generalization problem from adifferent perspective by categorizing underlying feature groups intodomain-shared and domain-specific features. Nevertheless, the domain-specificfeatures are difficult to be identified and distinguished from the input data.In this work, we propose DomaIn-SPEcific Liberating (DISPEL), a post-processingfine-grained masking approach that can filter out undefined andindistinguishable domain-specific features in the embedding space.Specifically, DISPEL utilizes a mask generator that produces a unique mask foreach input data to filter domain-specific features. The DISPEL framework ishighly flexible to be applied to any fine-tuned models. We derive ageneralization error bound to guarantee the generalization performance byoptimizing a designed objective loss. The experimental results on fivebenchmarks demonstrate DISPEL outperforms existing methods and can furthergeneralize various algorithms.

DISPEL: Domain Generalization via Domain-Specific Liberating