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. The previous work 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, was 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. We generate pseudo intermediate domains from normalizing flows and then use them for gradual domain adaptation. We evaluate our proposed method by experiments with 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，它通过利用从源域逐步过渡到目标域的中间域来实现。此前研究假设中间域数量充足且相邻域间距较小，因此可通过无标签数据集进行自训练的渐进式域自适应算法得以应用。然而在实际应用中，由于中间域数量有限且相邻域间距较大，渐进式自训练方法会失效。我们提出在保持无监督域自适应框架的前提下，利用标准化流解决该问题：通过标准化流生成伪中间域，并将其用于渐进式域自适应。通过真实数据集的实验评估，我们证实该方法能缓解上述问题并提升分类性能。