  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.
