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Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer
March 21, 2024, 4:46 a.m. | Yuang Ai, Xiaoqiang Zhou, Huaibo Huang, Lei Zhang, Ran He
cs.CV updates on arXiv.org arxiv.org
Abstract: Unsupervised Domain Adaptation (UDA) can effectively address domain gap issues in real-world image Super-Resolution (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 …
abstract arxiv augmentation cs.cv data domain domain adaptation framework free gap image practical privacy privacy policies restrictions soda source data transformer type uncertainty unsupervised wavelet world
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