Adversarial domain adaptation being aware of class relationships

Zeya Wang, Baoyu Jing, Yang Ni, Nanqing Dong, Pengtao Xie, Eric Xing

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

7 Scopus citations

Abstract

Adversarial training is a useful approach to promote the learning of transferable representations across the source and target domains, which has been widely applied for domain adaptation (DA) tasks based on deep neural networks. Until very recently, existing adversarial domain adaptation (ADA) methods ignore the useful information from the label space, which is an important factor accountable for the complicated data distributions associated with different semantic classes. Especially, the inter-class semantic relationships have been rarely considered and discussed in the current work of transfer learning. In this paper, we propose a novel relationship-aware adversarial domain adaptation (RADA) algorithm, which first utilizes a single multi-class domain discriminator to enforce the learning of inter-class dependency structure during domain-adversarial training and then aligns this structure with the inter-class dependencies that are characterized from training the label predictor on source domain. Specifically, we impose a regularization term to penalize the structure discrepancy between the inter-class dependencies respectively estimated from domain discriminator and label predictor. Through this alignment, our proposed method makes the adversarial domain adaptation aware of the class relationships. Empirical studies show that the incorporation of class relationships significantly improves the performance on benchmark datasets.

Original languageEnglish
Title of host publicationECAI 2020 - 24th European Conference on Artificial Intelligence, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Proceedings
EditorsGiuseppe De Giacomo, Alejandro Catala, Bistra Dilkina, Michela Milano, Senen Barro, Alberto Bugarin, Jerome Lang
PublisherIOS Press BV
Pages1579-1586
Number of pages8
ISBN (Electronic)9781643681009
DOIs
StatePublished - Aug 24 2020
Event24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Santiago de Compostela, Online, Spain
Duration: Aug 29 2020Sep 8 2020

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume325
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

Conference24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020
Country/TerritorySpain
CitySantiago de Compostela, Online
Period8/29/209/8/20

Bibliographical note

Publisher Copyright:
© 2020 The authors and IOS Press.

ASJC Scopus subject areas

  • Artificial Intelligence

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