论文标题
通过标准化流量逐渐适应域
Gradual Domain Adaptation via Normalizing Flows
论文作者
论文摘要
当源和目标域之间存在较大的差距时，标准域的适应方法无法正常工作。逐渐的域适应性是解决问题的方法之一。它涉及利用中间域，该域逐渐从源域转移到目标域。在先前的工作中，假定中间域的数量很大，并且相邻域之间的距离很小。因此，适用于无标记的数据集的自我训练的逐渐域适应算法是适用的。但是，实际上，逐渐的自我训练会失败，因为中间域的数量有限，并且相邻域之间的距离很大。我们建议使用归一化流量来解决此问题，同时保持无监督域的适应性框架。提出的方法通过源域学习了从目标域的分布到高斯混合物分布的转换。我们通过使用现实世界数据集的实验来评估我们提出的方法，并确认它可以减轻上述问题并改善分类性能。
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.
