University of Oulu

L. Zhao, W. Deng, G. Kuang, D. Hu and L. Liu, "Transferable Discriminative Feature Mining For Unsupervised Domain Adaptation," 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA, 2021, pp. 1259-1263, doi: 10.1109/ICIP42928.2021.9506534.

Transferable discriminative feature mining for unsupervised domain adaptation

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Author: Zhao, Lingjun1; Deng, Wanxia1; Kuang, Gangyao1;
Organizations: 1CEMEE, College of Electronic Science, National University of Defense Technology, China
2National University of Defense Technology, China
3Univeristy of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2023-04-12


Unsupervised Domain Adaptation (UDA) aims to seek an effective model for unlabeled target domain by leveraging knowledge from a labeled source domain with a related but different distribution. Many existing approaches ignore the underlying discriminative features of the target data and the discrepancy of conditional distributions. To address these two issues simultaneously, the paper presents a Transferable Discriminative Feature Mining (TDFM) approach for UDA, which can naturally unify the mining of domain-invariant discriminative features and the alignment of class-wise features into one single framework. To be specific, to achieve the domain-invariant discriminative features, TDFM jointly learns a shared encoding representation for two tasks: supervised classification of labeled source data, and discriminative clustering of unlabeled target data. It then conducts the class-wise alignment by decreasing intra-class variations and increasing inter-class differences across domains, encouraging the emergence of transferable discriminative features. When combined, these two procedures are mutually beneficial. Comprehensive experiments verify that TDFM can obtain remarkable margins over state-of-the-art domain adaptation methods.

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Series: IEEE International Conference on Image Processing
ISSN: 1522-4880
ISSN-E: 2381-8549
ISSN-L: 1522-4880
ISBN: 978-1-6654-4115-5
ISBN Print: 978-1-6654-3102-6
Pages: 1259 - 1263
DOI: 10.1109/icip42928.2021.9506534
Host publication: 2021 IEEE International Conference on Image Processing : Proceedings : 19–22 September 2021 Anchorage, Alaska, USA
Conference: IEEE International Conference on Image Processing
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
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