Alleviating the Equilibrium Challenge with Sample Virtual Labeling for Adversarial Domain Adaptation

Many domain adaptive object detection (DAOD) methods employ domain adversarial training to align features and mitigate the domain gap. In this approach, a feature extractor is trained to deceive a domain classifier, thereby aligning feature distributions. However, the domain classifier's discrimination capability can easily fall into a local optimum due to the equilibrium challenge, hindering the effective training of the feature extractor. In this work, we propose an efficient optimization strategy called Virtual-label Fooled Domain Discrimination (VFDD), which revitalizes the domain classifier during training using virtual domain labels. Such virtual label makes the separable distributions less separable, and thus leads to a more easily confused domain classifier, which in turn further drives feature alignment. Particularly, we introduce a novel concept of virtual domain label for the unaligned samples and propose the VirtualH -divergence to overcome the problem of falling into local optimum due to the equilibrium challenge. VFDD is orthogonal to most existing DAOD methods and can be integrated as a plug-and-play module to enhance these models. Theoretical insights and experimental analyses demonstrate that VFDD improves many popular baselines and surpasses recent unsupervised DAOD models.