  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.
