Domain Generalization by Learning and Removing Domain-specific Features

• URL: http://arxiv.org/abs/2212.07101v1
• Date: Wed, 14 Dec 2022 08:46:46 GMT
• Title: Domain Generalization by Learning and Removing Domain-specific Features
• Authors: Yu Ding, Lei Wang, Bin Liang, Shuming Liang, Yang Wang, Fang Chen
• Abstract summary: Domain generalization aims to tackle this issue by learning a model that can generalize to unseen domains. We propose a new approach that aims to explicitly remove domain-specific features for domain generalization. We develop an encoder-decoder network to map each input image into a new image space where the learned domain-specific features are removed.
• Score: 15.061481139046952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Neural Networks (DNNs) suffer from domain shift when the test dataset follows a distribution different from the training dataset. Domain generalization aims to tackle this issue by learning a model that can generalize to unseen domains. In this paper, we propose a new approach that aims to explicitly remove domain-specific features for domain generalization. Following this approach, we propose a novel framework called Learning and Removing Domain-specific features for Generalization (LRDG) that learns a domain-invariant model by tactically removing domain-specific features from the input images. Specifically, we design a classifier to effectively learn the domain-specific features for each source domain, respectively. We then develop an encoder-decoder network to map each input image into a new image space where the learned domain-specific features are removed. With the images output by the encoder-decoder network, another classifier is designed to learn the domain-invariant features to conduct image classification. Extensive experiments demonstrate that our framework achieves superior performance compared with state-of-the-art methods.

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