Qian Wang qian.wang@durham.ac.uk
Academic Visitor
Qian Wang qian.wang@durham.ac.uk
Academic Visitor
Professor Toby Breckon toby.breckon@durham.ac.uk
Professor
Domain adaptation aims to exploit useful information from the source domain where annotated training data are easier to obtain to address a learning problem in the target domain where only limited or even no annotated data are available. In classification problems, domain adaptation has been studied under the assumption all classes are available in the target domain regardless of the annotations. However, a common situation where only a subset of classes in the target domain are available has not attracted much attention. In this paper, we formulate this particular domain adaptation problem within a generalized zero-shot learning framework by treating the labelled source-domain samples as semantic representations for zero-shot learning. For this novel problem, neither conventional domain adaptation approaches nor zero-shot learning algorithms directly apply. To solve this problem, we present a novel Coupled Conditional Variational Autoencoder (CCVAE) which can generate synthetic target-domain image features for unseen classes from real images in the source domain. Extensive experiments have been conducted on three domain adaptation datasets including a bespoke X-ray security checkpoint dataset to simulate a real-world application in aviation security. The results demonstrate the effectiveness of our proposed approach both against established benchmarks and in terms of real-world applicability.
Wang, Q., & Breckon, T. (2023). Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders. Neural Networks, 163, 40-52. https://doi.org/10.1016/j.neunet.2023.03.033
Journal Article Type | Article |
---|---|
Acceptance Date | Mar 22, 2023 |
Online Publication Date | Mar 28, 2023 |
Publication Date | 2023-06 |
Deposit Date | Apr 18, 2023 |
Publicly Available Date | Apr 19, 2023 |
Journal | Neural Networks |
Print ISSN | 0893-6080 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 163 |
Pages | 40-52 |
DOI | https://doi.org/10.1016/j.neunet.2023.03.033 |
Public URL | https://durham-repository.worktribe.com/output/1176004 |
Published Journal Article
(1.4 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by-nc-nd/4.0/
Copyright Statement
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
On Fine-tuned Deep Features for Unsupervised Domain Adaptation
(2023)
Presentation / Conference Contribution
Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery
(2021)
Presentation / Conference Contribution
Source Class Selection with Label Propagation for Partial Domain Adaptation
(2021)
Presentation / Conference Contribution
A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks
(2019)
Presentation / Conference Contribution
Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition
(2019)
Presentation / Conference Contribution
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
Apache License Version 2.0 (http://www.apache.org/licenses/)
Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search