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May, 2024
基于中值随机平滑的通用稳健方法用于现实世界超分辨率
Universal Robustness via Median Randomized Smoothing for Real-World Super-Resolution
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Zakariya Chaouai, Mohamed Tamaazousti
TL;DR
我们的研究探索了提高深度学习超分辨率模型鲁棒性的不同方法的普适性,并发现中值随机平滑在对抗攻击以及标准图像破坏方面比其他方法更具通用性。这些结果支持将真实世界超分辨率方法的发展重心转向鲁棒超分辨率。
Abstract
Most of the recent literature on
image super-resolution
(SR) can be classified into two main approaches. The first one involves learning a
corruption model
tailored to a specific dataset, aiming to mimic the nois
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