Collaborative representation with curriculum classifier boosting for unsupervised domain adaptation. (May 2021)

• Record Type: Journal Article
• Authors: Han, Chao; Zhou, Deyun; Xie, Yu; Gong, Maoguo; Lei, Yu; Shi, Jiao
• Is Part Of: Pattern recognition. Volume 113(2021)
• Publication Date: 2021-05
• Subjects: Domain adaptation -- Collaborative representation -- Curriculum learning -- Classifier boosting; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107802
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We present a novel unsupervised domain adaptation solution based on collaborative representation which seeks for the close samples between domains and uses them to assist further predictions. Plenty of experiments validate the effectiveness of our method and more general, we can solve domain adaptation problems without reducing domain discrepancy explicitly, which is different from previous methods. Curriculum sample choosing is proposed to select the close samples between domains based on reconstruction residual. Then these samples are added to training set for subsequent prediction. We propose distance-aware sparsity regularization to learn more reasonable representation, so that samples have smaller distance to the query sample are intended to have larger weights. Abstract: Domain adaptation aims at leveraging rich knowledge in the source domain to build an accurate classifier in the different but related target domain. Most prior methods attempt to align features or reduce domain discrepancy by means of statistical properties yet ignore the differences among samples. In this paper, we put forward a novel solution based on collaborative representation for classifier adaptation. Similar to instance re-weighting, we aim to learn an adaptive classifier by multi-stage inference and instance rearranging. Specifically, a curriculum learning based sample selection scheme is proposed, then the chosen samples are integrated into training set iteratively. Due to the distribution mismatch of two domains, we propose distance-aware sparsity regularization to learn more flexible representations. Extensive experiments verify that the proposed method is comparable or superior to the state-of-the-art methods.