Domain adaptation deals with adapting classifiers trained on data from a source distribution, to work effectively on data from a target distribution. In this paper, we introduce the Nonlinear Embedding Transform (NET) for unsupervised domain adaptation. The NET reduces cross-domain disparity through nonlinear domain alignment. It also embeds the domain-aligned data such that similar data points are clustered together. This results in enhanced classification. To determine the parameters in the NET model (and in other unsupervised domain adaptation models), we introduce a validation procedure by sampling source data points that are similar in distribution to the target data. We test the NET and the validation procedure using popular image datasets and compare the classification results across competitive procedures for unsupervised domain adaptation.

本文介绍了一个基于非线性嵌入变换的无监督域自适应方法，通过域对齐降低跨域差异，降低数据分布间的偏移，使得相似的数据点可以聚类在一起，提高了分类的性能，并提出了基于样本抽样的验证过程，以确定无监督域适应模型的参数，最后在多个图像数据集上对比了该方法和其他竞争算法的分类结果。

非线性嵌入用于无监督域适应的模型选择