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