DISPEL: Domain Generalization via Domain-Specific Liberating

• URL: http://arxiv.org/abs/2307.07181v3
• Date: Tue, 1 Aug 2023 00:45:24 GMT
• Title: DISPEL: Domain Generalization via Domain-Specific Liberating
• Authors: Chia-Yuan Chang, Yu-Neng Chuang, Guanchu Wang, Mengnan Du, Na Zou
• Abstract summary: Domain generalization aims to learn a model that can perform well on unseen test domains by only training on limited source domains. 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.
• Score: 19.21625050855744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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