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On Fine-tuned Deep Features for Unsupervised Domain Adaptation

Wang, Q.; Meng, F.; Breckon, T.P.

On Fine-tuned Deep Features for Unsupervised Domain Adaptation Thumbnail


F. Meng


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.


Wang, Q., Meng, F., & Breckon, T. (2023). On Fine-tuned Deep Features for Unsupervised Domain Adaptation. .

Conference Name IJCNN 2023: International Joint Conference on Neural Networks
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
Public URL
Publisher URL


Accepted Conference Proceeding (561 Kb)

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