  In smart computing, the labels of training samples for a specific task are
not always abundant. However, the labels of samples in a relevant but different
dataset are available. As a result, researchers have relied on unsupervised
domain adaptation to leverage the labels in a dataset (the source domain) to
perform better classification in a different, unlabeled dataset (target
domain). Existing non-generative adversarial solutions for UDA aim at achieving
domain confusion through adversarial training. The ideal scenario is that
perfect domain confusion is achieved, but this is not guaranteed to be true. To
further enforce domain confusion on top of the adversarial training, we propose
a novel UDA algorithm, \textit{E-ADDA}, which uses both a novel variation of
the Mahalanobis distance loss and an out-of-distribution detection subroutine.
The Mahalanobis distance loss minimizes the distribution-wise distance between
the encoded target samples and the distribution of the source domain, thus
enforcing additional domain confusion on top of adversarial training. Then, the
OOD subroutine further eliminates samples on which the domain confusion is
unsuccessful. We have performed extensive and comprehensive evaluations of
E-ADDA in the acoustic and computer vision modalities. In the acoustic
modality, E-ADDA outperforms several state-of-the-art UDA algorithms by up to
29.8%, measured in the f1 score. In the computer vision modality, the
evaluation results suggest that we achieve new state-of-the-art performance on
popular UDA benchmarks such as Office-31 and Office-Home, outperforming the
second best-performing algorithms by up to 17.9%.
