  Domain generalization aims to learn a generalization model that can perform
well on unseen test domains by only training on limited source domains.
However, existing domain generalization approaches often bring in
prediction-irrelevant noise or require the collection of domain labels. To
address these challenges, we consider the domain generalization problem from a
different perspective by categorizing underlying feature groups into
domain-shared and domain-specific features. Nevertheless, the domain-specific
features are difficult to be identified and distinguished from the input data.
In this work, we propose DomaIn-SPEcific Liberating (DISPEL), a post-processing
fine-grained masking approach that can filter out undefined and
indistinguishable domain-specific features in the embedding space.
Specifically, DISPEL utilizes a mask generator that produces a unique mask for
each input data to filter domain-specific features. The DISPEL framework is
highly flexible to be applied to any fine-tuned models. We derive a
generalization error bound to guarantee the generalization performance by
optimizing a designed objective loss. The experimental results on five
benchmarks demonstrate DISPEL outperforms existing methods and can further
generalize various algorithms.
