Mr Qian Wang qian.wang@durham.ac.uk
Academic Visitor
On Fine-tuned Deep Features for Unsupervised Domain Adaptation
Wang, Q.; Meng, F.; Breckon, T.P.
Authors
F. Meng
Professor Toby Breckon toby.breckon@durham.ac.uk
Professor
Abstract
Prior feature transformation based approaches to Unsupervised Domain Adaptation (UDA) employ the deep features extracted by pre-trained deep models without fine-tuning them on the specific source or target domain data for a particular domain adaptation task. In contrast, end-to-end learning based approaches optimise the pre-trained backbones and the customised adaptation modules simultaneously to learn domaininvariant features for UDA. In this work, we explore the potential of combining fine-tuned features and feature transformation based UDA methods for improved domain adaptation performance. Specifically, we integrate the prevalent progressive pseudo-labelling techniques into the fine-tuning framework to extract fine-tuned features which are subsequently used in a state-of-the-art feature transformation based domain adaptation method SPL (Selective Pseudo-Labeling). Thorough experiments with multiple deep models including ResNet-50/101 and DeiTsmall/base are conducted to demonstrate the combination of finetuned features and SPL can achieve state-of-the-art performance on several benchmark datasets.
Citation
Wang, Q., Meng, F., & Breckon, T. (2023). On Fine-tuned Deep Features for Unsupervised Domain Adaptation. . https://doi.org/10.1109/IJCNN54540.2023.10191262
Conference Name | IJCNN 2023: International Joint Conference on Neural Networks |
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Conference Location | Queensland, Australia |
Start Date | Jun 18, 2023 |
End Date | Jun 23, 2023 |
Acceptance Date | Mar 30, 2023 |
Online Publication Date | Aug 2, 2023 |
Publication Date | 2023-08 |
Deposit Date | Jun 2, 2023 |
Publicly Available Date | Sep 13, 2023 |
Publisher | Institute of Electrical and Electronics Engineers |
Series Title | IJCNN 2023 Conference Proceedings |
Series ISSN | 2161-4407 |
DOI | https://doi.org/10.1109/IJCNN54540.2023.10191262 |
Public URL | https://durham-repository.worktribe.com/output/1134732 |
Publisher URL | https://ieeexplore.ieee.org/xpl/conhome/1000500/all-proceedings |
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