Causality-based Dual-Contrastive Learning Framework for Domain Generalization

• URL: http://arxiv.org/abs/2301.09120v2
• Date: Wed, 22 Mar 2023 06:41:10 GMT
• Title: Causality-based Dual-Contrastive Learning Framework for Domain Generalization
• Authors: Zining Chen, Weiqiu Wang, Zhicheng Zhao, Aidong Men
• Abstract summary: Domain Generalization (DG) is essentially a sub-branch of out-of-distribution generalization. In this paper, we propose a Dual-Contrastive Learning (DCL) module on feature and prototype contrast. We also introduce a Similarity-based Hard-pair Mining (SHM) strategy to leverage information on diversity shift.
• Score: 16.81075442901155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain Generalization (DG) is essentially a sub-branch of out-of-distribution generalization, which trains models from multiple source domains and generalizes to unseen target domains. Recently, some domain generalization algorithms have emerged, but most of them were designed with non-transferable complex architecture. Additionally, contrastive learning has become a promising solution for simplicity and efficiency in DG. However, existing contrastive learning neglected domain shifts that caused severe model confusions. In this paper, we propose a Dual-Contrastive Learning (DCL) module on feature and prototype contrast. Moreover, we design a novel Causal Fusion Attention (CFA) module to fuse diverse views of a single image to attain prototype. Furthermore, we introduce a Similarity-based Hard-pair Mining (SHM) strategy to leverage information on diversity shift. Extensive experiments show that our method outperforms state-of-the-art algorithms on three DG datasets. The proposed algorithm can also serve as a plug-and-play module without usage of domain labels.

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