We study the problem of unsupervised domain adaptation which aims to adapt models trained on a labeled source domain to a completely unlabeled target domain. Domain adversarial training is a promising approach and has been a basis for many state-of-the-art approaches in unsupervised domain adaptation. The idea of domain adversarial training is to align the feature space between the source domain and target domain by adversarially training a domain classifier and a feature encoder. Recently, cluster assumption has been applied to unsupervised domain adaptation and achieved strong performance. In this paper, we propose a new regularization method called Virtual Mixup Training (VMT), which is able to further constrain the hypothesis of cluster assumption. The idea of VMT is to impose a locally-Lipschitz constraint on the model by smoothing the output distribution along the lines between pairs of training samples. Unlike the traditional mixup model, our method constructs the combination samples without label information, allowing it to be applicable to unsupervised domain adaptation. The proposed method is generic and can be combined with existing methods using domain adversarial training. We combine VMT with a recent state-of-the-art model called VADA, and extensive experiments demonstrate that VMT significantly improves the performance of VADA on several domain adaptation benchmark datasets. For the challenging task of adapting MNIST to SVHN, when not using instance normalization, VMT improves the accuracy of VADA by over 30%. When using instance normalization, our model achieves an accuracy of 96.4%, which is very close to the accuracy (96.5%) of the train-on-target model. Code will be made publicly available.

本文提出一种叫做Virtual Mixup Training的新方法，该方法可以将局部Lipschitz约束性应用到训练数据间距的区域中，不需要使用标签信息，适用于无监督域自适应。结果表明，VMT显著提高了VADA在六个领域适应基准数据集上的性能，尤其是将MNIST适应到SVHN的挑战性任务中，VMT可以将VADA的准确性提高30%以上。

无监督域自适应的虚拟混合训练