@inproceedings{
shi2024alleviating,
title={Alleviating the Equilibrium Challenge with Sample Virtual Labeling for Adversarial Domain Adaptation},
author={Wenxu Shi and Bochuan Zheng},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=tWCOxa9lWs}
}
Numerous domain adaptive object detection (DAOD) methods leverage domain adversarial training to align the features to mitigate domain gap, where a feature extractor is trained to fool a domain classifier in order to have aligned feature distributions. The discrimination capability of the domain classifier is easy to fall into the  local optimum due to the equilibrium challenge, thus cannot effectively further drive the training of feature extractor. In this work, we propose an efficient optimization strategy called \underline{V}irtual-label \underline{F}ooled \underline{D}omain \underline{D}iscrimination (VFDD), which revitalizes the domain classifier during training using \emph{virtual} sample labels.  Such virtual sample 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 \emph{virtual} label for the unaligned samples and propose the \emph{Virtual}-$\mathcal{H}$-divergence to overcome the problem of falling into local optimum due to the equilibrium challenge. The proposed VFDD is orthogonal to most existing DAOD methods and can be used as a plug-and-play module to facilitate existing DAOD models. Theoretical insights and experimental analyses demonstrate that VFDD improves many popular baselines and also outperforms the recent unsupervised domain adaptive object detection models.
Alleviating the Equilibrium Challenge with Sample Virtual Labeling for Adversarial Domain Adaptation
Wenxu Shi Bochuan Zheng