More is Better: A Novel Multi-view Framework for Domain Generalization

• URL: http://arxiv.org/abs/2112.12329v1
• Date: Thu, 23 Dec 2021 02:51:35 GMT
• Title: More is Better: A Novel Multi-view Framework for Domain Generalization
• Authors: Jian Zhang, Lei Qi, Yinghuan Shi, Yang Gao
• Abstract summary: Key issue of domain generalization (DG) is how to prevent overfitting to the observed source domains. By treating tasks and images as different views, we propose a novel multi-view DG framework. In test stage, to alleviate unstable prediction, we utilize multiple augmented images to yield multi-view prediction.
• Score: 28.12350681444117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aiming to generalize the model trained in source domains to unseen target domains, domain generalization (DG) has attracted lots of attention recently. The key issue of DG is how to prevent overfitting to the observed source domains because target domain is unavailable during training. We investigate that overfitting not only causes the inferior generalization ability to unseen target domains but also leads unstable prediction in the test stage. In this paper, we observe that both sampling multiple tasks in training stage and generating augmented images in test stage largely benefit generalization performance. Thus, by treating tasks and images as different views, we propose a novel multi-view DG framework. Specifically, in training stage, to enhance generalization ability, we develop a multi-view regularized meta-learning algorithm that employs multiple tasks to produce a suitable optimization direction during updating model. In test stage, to alleviate unstable prediction, we utilize multiple augmented images to yield multi-view prediction, which significantly promotes model reliability via fusing the results of different views of a test image. Extensive experiments on three benchmark datasets validate our method outperforms several state-of-the-art approaches.

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