  We consider unsupervised domain adaptation: given labelled examples from a
source domain and unlabelled examples from a related target domain, the goal is
to infer the labels of target examples. Under the assumption that features from
pre-trained deep neural networks are transferable across related domains,
domain adaptation reduces to aligning source and target domain at class
prediction uncertainty level. We tackle this problem by introducing a method
based on adversarial learning which forces the label uncertainty predictions on
the target domain to be indistinguishable from those on the source domain.
Pre-trained deep neural networks are used to generate deep features having high
transferability across related domains. We perform an extensive experimental
analysis of the proposed method over a wide set of publicly available
pre-trained deep neural networks. Results of our experiments on domain
adaptation tasks for image classification show that class prediction
uncertainty alignment with features extracted from pre-trained deep neural
networks provides an efficient, robust and effective method for domain
adaptation.
