Progressive Target-Styled Feature Augmentation for Unsupervised Domain
  Adaptation on Point Clouds
Zicheng Wang Zhen Zhao Yiming Wu Luping Zhou Dong Xu
Unsupervised domain adaptation is a critical challenge in the field of point
cloud analysis, as models trained on one set of data often struggle to perform
well in new scenarios due to domain shifts. Previous works tackle the problem
by using adversarial training or self-supervised learning for feature extractor
adaptation, but ensuring that features extracted from the target domain can be
distinguished by the source-supervised classifier remains challenging. In this
work, we propose a novel approach called progressive target-styled feature
augmentation (PTSFA). Unlike previous works that focus on feature extractor
adaptation, our PTSFA approach focuses on classifier adaptation. It aims to
empower the classifier to recognize target-styled source features and
progressively adapt to the target domain. To enhance the reliability of
predictions within the PTSFA framework and encourage discriminative feature
extraction, we further introduce a new intermediate domain approaching (IDA)
strategy. We validate our method on the benchmark datasets, where our method
achieves new state-of-the-art performance. Our code is available at
https://github.com/xiaoyao3302/PTSFA.