This repository is an official PyTorch implementation of the paper Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer.
Yuang Ai1,2, Xiaoqiang Zhou1,3, Huaibo Huang1,2, Lei Zhang4,5, Ran He1,2,6
1MAIS & CRIPAC, Institute of Automation, Chinese Academy of Sciences
2School of Artificial Intelligence, University of Chinese Academy of Sciences
3University of Science and Technology of China 4OPPO Research Institute
5The Hong Kong Polytechnic University 6ShanghaiTech University
Unsupervised Domain Adaptation (UDA) can effectively address domain gap issues in real-world image SuperResolution (SR) by accessing both the source and target data. Considering privacy policies or transmission restrictions of source data in practical scenarios, we propose a SOurce-free Domain Adaptation framework for image SR (SODA-SR) to address this issue, i.e., adapt a source-trained model to a target domain with only unlabeled target data. SODA-SR leverages the source-trained model to generate refined pseudo-labels for teacher-student learning. To better utilize pseudo-labels, we propose a novel wavelet-based augmentation method, named Wavelet Augmentation Transformer (WAT), which can be flexibly incorporated with existing networks, to implicitly produce useful augmented data. WAT learns low-frequency information of varying levels across diverse samples, which is aggregated efficiently via deformable attention. Furthermore, an uncertainty-aware self-training mechanism is proposed to improve the accuracy of pseudo-labels, with inaccurate predictions being rectified by uncertainty estimation. To acquire better SR results and avoid overfitting pseudo-labels, several regularization losses are proposed to constrain target LR and SR images in the frequency domain. Experiments show that without accessing source data, SODA-SR outperforms state-of-the-art UDA methods in both synthetic→real and real→real adaptation settings, and is not constrained by specific network architectures.
Quantitative Results
Visual Comparisons
Our code is built upon KAIR, Deformable-DETR and CDC. We thank the authors for their awesome work.
If you have any questions, please feel free to concat with me at shallowdream555@gmail.com.
@inproceedings{ai2024uncertainty,
title={Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer},
author={Ai, Yuang and Zhou, Xiaoqiang and Huang, Huaibo and Zhang, Lei and He, Ran},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={8142--8152},
year={2024}
}