Domain-invariant Feature Exploration for Domain Generalization

• URL: http://arxiv.org/abs/2207.12020v1
• Date: Mon, 25 Jul 2022 09:55:55 GMT
• Title: Domain-invariant Feature Exploration for Domain Generalization
• Authors: Wang Lu, Jindong Wang, Haoliang Li, Yiqiang Chen, Xing Xie
• Abstract summary: We argue that domain-invariant features should be originating from both internal and mutual sides. We propose DIFEX for Domain-Invariant Feature EXploration. Experiments on both time-series and visual benchmarks demonstrate that the proposed DIFEX achieves state-of-the-art performance.
• Score: 35.99082628524934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning has achieved great success in the past few years. However, the performance of deep learning is likely to impede in face of non-IID situations. Domain generalization (DG) enables a model to generalize to an unseen test distribution, i.e., to learn domain-invariant representations. In this paper, we argue that domain-invariant features should be originating from both internal and mutual sides. Internal invariance means that the features can be learned with a single domain and the features capture intrinsic semantics of data, i.e., the property within a domain, which is agnostic to other domains. Mutual invariance means that the features can be learned with multiple domains (cross-domain) and the features contain common information, i.e., the transferable features w.r.t. other domains. We then propose DIFEX for Domain-Invariant Feature EXploration. DIFEX employs a knowledge distillation framework to capture the high-level Fourier phase as the internally-invariant features and learn cross-domain correlation alignment as the mutually-invariant features. We further design an exploration loss to increase the feature diversity for better generalization. Extensive experiments on both time-series and visual benchmarks demonstrate that the proposed DIFEX achieves state-of-the-art performance.

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