Discrepancy-Aware Collaborative Representation for Unsupervised Domain Adaptation

Chao Han, Deyun Zhou, Yu Xie, Yu Lei, Jiao Shi, Maoguo Gong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Domain adaptation aims at learning from the la-beled source domain to build an accurate classifier for a related but different target domain. Existing methods attempt to reduce domain discrepancy explicitly by means of statistical properties yet ignore the inherent differences among samples. In this paper, we present a novel solution for domain adaptation based on collaborative representation, named Discrepancy-Aware Collaborative Representation (DACR). Inspired by the success of nearest regularization, DACR develops a novel indicator to measure the discrepancy among every source sample and target domain. Then the indicator is employed in sparse regularization thus ensure that samples with small discrepancy have larger weights in the learned representation. Extensive experiments verify that DACR is able to achieve comparable performance with existing methods while significantly reducing computing complexity.

Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728169262
DOIs
StatePublished - Jul 2020
Event2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2020 International Joint Conference on Neural Networks, IJCNN 2020
Country/TerritoryUnited Kingdom
CityVirtual, Glasgow
Period19/07/2024/07/20

Keywords

  • Collaborative Representation
  • Domain Adaptation
  • Transfer Learning

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