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巨大差异时效果不佳。渐进式域自适应是解决该问题的方法之一，其核心思想是利用从源域逐步过渡到目标域的中间域。先前研究假设中间域数量充足且相邻域间距离较小，因此适用于结合未标注数据自训练的渐进式域自适应算法。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。我们提出在保持无监督域自适应框架的前提下，运用正则化流技术解决该问题。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。通过在真实数据集上的实验评估，我们证实该方法能有效缓解上述问题并提升分类性能。