  A novel approach for unsupervised domain adaptation for neural networks is
proposed. It relies on metric-based regularization of the learning process. The
metric-based regularization aims at domain-invariant latent feature
representations by means of maximizing the similarity between domain-specific
activation distributions. The proposed metric results from modifying an
integral probability metric such that it becomes less translation-sensitive on
a polynomial function space. The metric has an intuitive interpretation in the
dual space as the sum of differences of higher order central moments of the
corresponding activation distributions. Under appropriate assumptions on the
input distributions, error minimization is proven for the continuous case. As
demonstrated by an analysis of standard benchmark experiments for sentiment
analysis, object recognition and digit recognition, the outlined approach is
robust regarding parameter changes and achieves higher classification
accuracies than comparable approaches. The source code is available at
https://github.com/wzell/mann.
