Feature Alignment and Restoration for Domain Generalization and Adaptation

• URL: http://arxiv.org/abs/2006.12009v1
• Date: Mon, 22 Jun 2020 05:08:13 GMT
• Title: Feature Alignment and Restoration for Domain Generalization and Adaptation
• Authors: Xin Jin, Cuiling Lan, Wenjun Zeng, Zhibo Chen
• Abstract summary: Cross domain feature alignment has been widely explored to pull the feature distributions of different domains in order to learn domain-invariant representations. We propose a unified framework termed Feature Alignment and Restoration (FAR) to simultaneously ensure high generalization and discrimination power of the networks. Experiments on multiple classification benchmarks demonstrate the high performance and strong generalization of our FAR framework for both domain generalization and unsupervised domain adaptation.
• Score: 93.39253443415392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For domain generalization (DG) and unsupervised domain adaptation (UDA), cross domain feature alignment has been widely explored to pull the feature distributions of different domains in order to learn domain-invariant representations. However, the feature alignment is in general task-ignorant and could result in degradation of the discrimination power of the feature representation and thus hinders the high performance. In this paper, we propose a unified framework termed Feature Alignment and Restoration (FAR) to simultaneously ensure high generalization and discrimination power of the networks for effective DG and UDA. Specifically, we perform feature alignment (FA) across domains by aligning the moments of the distributions of attentively selected features to reduce their discrepancy. To ensure high discrimination, we propose a Feature Restoration (FR) operation to distill task-relevant features from the residual information and use them to compensate for the aligned features. For better disentanglement, we enforce a dual ranking entropy loss constraint in the FR step to encourage the separation of task-relevant and task-irrelevant features. Extensive experiments on multiple classification benchmarks demonstrate the high performance and strong generalization of our FAR framework for both domain generalization and unsupervised domain adaptation.

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