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 the source and target domains in the high-dimensional homogeneous feature space without explicit domain alignment. To this end, we employ the effective Selective Pseudo-Labelling (SPL) technique 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 \textit{norm-AE} to generate synthetic features for the target domain as a data augmentation strategy to enhance the 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 \textit{norm-AE} for feature augmentation can further improve the performance in most cases. As a result, our proposed methods (i.e. \textit{naive-SPL} and \textit{norm-AE-SPL}) can achieve comparable performance with state-of-the-art methods with the average accuracy of 93.4% and 90.4% on Office-Caltech and ImageCLEF-DA datasets, and achieve competitive performance on Digits, Office31 and Office-Home datasets with the average accuracy of 97.2%, 87.6% and 68.6% respectively.
@article{wang23augmentation,
author = {Wang, Q. and Meng, F. and Breckon, T.P.},
title = {Data Augmentation with norm-AE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation},
journal = {Neural Networks},
volume={161},
pages={614--625},
year = {2023},
month = {February},
publisher = {Elsevier},
keywords = {unsupervised domain adaptation, data augmentation, variational autoencoder, selective pseudo-labelling},
url = {https://breckon.org/toby/publications/papers/wang23augmentation.pdf},
arxiv = {https://arxiv.org/abs/2012.00848},
note = {to appear},
category = {imageclass},
}