通过归一化流实现渐进式域适应
Gradual Domain Adaptation via Normalizing Flows
AI总结：
针对中间域有限且相邻域差距较大导致渐进式自训练失效的问题，提出基于归一化流的无监督域适应方法，通过源域学习目标域分布到高斯混合分布的转换，实验证明可提升分类性能。
摘要：
当源域与目标域之间存在较大差距时，标准的域适应方法效果不佳。渐进式域适应是解决该问题的方法之一，它涉及利用从源域逐渐向目标域转移的中间域。在以往的工作中，通常假设中间域数量较多且相邻域之间的距离较小，因此涉及使用无标记数据集进行自训练的渐进式域适应算法是适用的。然而在实践中，由于中间域数量有限且相邻域之间距离较大，渐进式自训练将会失败。我们提出使用归一化流来处理这一问题，同时保持无监督域适应的框架。所提出的方法通过源域学习从目标域分布到高斯混合分布的转换。我们使用真实数据集通过实验评估了所提出的方法，并证实它缓解了上述问题，提高了分类性能。
英文摘要：
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.
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