  We address the Unsupervised Domain Adaptation (UDA) problem in image
classification from a new perspective. In contrast to most existing works which
either align the data distributions or learn domain-invariant features, we
directly learn a unified classifier for both domains within a high-dimensional
homogeneous feature space without explicit domain adaptation. To this end, we
employ the effective Selective Pseudo-Labelling (SPL) techniques to take
advantage of the unlabelled samples in the target domain. Surprisingly, data
distribution discrepancy across the source and target domains can be well
handled by a computationally simple classifier (e.g., a shallow Multi-Layer
Perceptron) trained in the original feature space. Besides, we propose a novel
generative model norm-VAE to generate synthetic features for the target domain
as a data augmentation strategy to enhance classifier training. Experimental
results on several benchmark datasets demonstrate the pseudo-labelling strategy
itself can lead to comparable performance to many state-of-the-art methods
whilst the use of norm-VAE for feature augmentation can further improve the
performance in most cases. As a result, our proposed methods (i.e. naive-SPL
and norm-VAE-SPL) can achieve new state-of-the-art performance with the average
accuracy of 93.4% and 90.4% on Office-Caltech and ImageCLEF-DA datasets, and
comparable performance on Digits, Office31 and Office-Home datasets with the
average accuracy of 97.2%, 87.6% and 67.9% respectively.
