On Fine-Tuned Deep Features for Unsupervised Domain Adaptation
This work addresses domain adaptation for machine learning applications where labeled data is scarce, presenting an incremental improvement by integrating fine-tuning with existing methods.
The paper tackles the problem of unsupervised domain adaptation by combining fine-tuned deep features with feature transformation methods, achieving state-of-the-art performance on benchmark datasets using models like ResNet-50/101 and DeiT-small/base.
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 domain-invariant 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 DeiT-small/base are conducted to demonstrate the combination of fine-tuned features and SPL can achieve state-of-the-art performance on several benchmark datasets.