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Deep Neural Networks have exhibited considerable success in various visualtasks. However, when applied to unseen test datasets, state-of-the-art modelsoften suffer performance degradation due to domain shifts. In this paper, weintroduce a novel approach for domain generalization from a novel perspectiveof enhancing the robustness of channels in feature maps to domain shifts. Weobserve that models trained on source domains contain a substantial number ofchannels that exhibit unstable activations across different domains, which areinclined to capture domain-specific features and behave abnormally when exposedto unseen target domains. To address the issue, we propose a DomainDropframework to continuously enhance the channel robustness to domain shifts,where a domain discriminator is used to identify and drop unstable channels infeature maps of each network layer during forward propagation. We theoreticallyprove that our framework could effectively lower the generalization bound.Extensive experiments on several benchmarks indicate that our frameworkachieves state-of-the-art performance compared to other competing methods. Ourcode is available at https://github.com/lingeringlight/DomainDrop.

DomainDrop: Suppressing Domain-Sensitive Channels for Domain Generalization