2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IVASR-5.6
Paper Title TRANSFERABLE DISCRIMINATIVE FEATURE MINING FOR UNSUPERVISED DOMAIN ADAPTATION
Authors Lingjun Zhao, Wanxia Deng, Gangyao Kuang, Dewen Hu, National University of Defense Technology, China; Li Liu, University of Oulu, Finland
SessionMLR-APPL-IVASR-5: Machine learning for image and video analysis, synthesis, and retrieval 5
LocationArea C
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video analysis, synthesis, and retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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 classwise 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.