  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.

Machine Learning;Machine Learning