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FlexAdapt: Flexible cycle-consistent adversarial domain adaptation

Mathur, A; Isopoussu, A; Kawsar, F; Berthouze, NB; Lane, ND; (2020) FlexAdapt: Flexible cycle-consistent adversarial domain adaptation. In: (Proceedings) 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). (pp. pp. 896-901). IEEE: Boca Raton, FL, USA. Green open access

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Abstract

Unsupervised domain adaptation is emerging as a powerful technique to improve the generalizability of deep learning models to new image domains without using any labeled data in the target domain. In the literature, solutions which perform cross-domain feature-matching (e.g., ADDA), pixel-matching (CycleGAN), and combination of the two (e.g., CyCADA) have been proposed for unsupervised domain adaptation. Many of these approaches make a strong assumption that the source and target label spaces are the same, however in the real-world, this assumption does not hold true. In this paper, we propose a novel solution, FlexAdapt, which extends the state-of-the-art unsupervised domain adaptation approach of CyCADA to scenarios where the label spaces in source and target domains are only partially overlapped. Our solution beats a number of state-of-the-art baseline approaches by as much as 29% in some scenarios, and represent a way forward for applying domain adaptation techniques in the real world.

Type: Proceedings paper
Title: FlexAdapt: Flexible cycle-consistent adversarial domain adaptation
Event: 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICMLA.2019.00155
Publisher version: https://doi.org/10.1109/ICMLA.2019.00155
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > UCL Interaction Centre
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10093647
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