Standard domain adaptation methods do not work well when a large gap exists between the source and target domains. Gradual domain adaptation is one of the approaches used to address the problem. It involves leveraging the intermediate domain, which gradually shifts from the source domain to the target domain. In previous work, it is assumed that the number of intermediate domains is large and the distance between adjacent domains is small; hence, the gradual domain adaptation algorithm, involving self-training with unlabeled datasets, is applicable. In practice, however, gradual self-training will fail because the number of intermediate domains is limited and the distance between adjacent domains is large. We propose the use of normalizing flows to deal with this problem while maintaining the framework of unsupervised domain adaptation. The proposed method learns a transformation from the distribution of the target domain to the Gaussian mixture distribution via the source domain. We evaluate our proposed method by experiments using real-world datasets and confirm that it mitigates the above-explained problem and improves the classification performance.

翻译：标准域自适应方法在源域与目标域之间存在较大差距时效果不佳。渐进式域自适应是解决该问题的方法之一，其通过利用从源域逐步过渡到目标域的中间域来实现。先前的研究假设中间域数量充足且相邻域间距离较小，因此采用基于未标注数据集的自训练渐进式域自适应算法是可行的。然而在实际应用中，由于中间域数量有限且相邻域间距离较大，渐进式自训练方法将会失效。本文提出在无监督域自适应框架下使用归一化流解决该问题。所提方法通过源域学习从目标域分布到高斯混合分布的变换。我们通过真实数据集的实验对所提方法进行评估，验证了该方法能够缓解上述问题并提升分类性能。