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• W4287239779 abstract "Unlike the success of neural architecture search (NAS) in high-level vision tasks, it remains challenging to find computationally efficient and memory-efficient solutions to low-level vision problems such as image restoration through NAS. One of the fundamental barriers to differential NAS-based image restoration is the optimization gap between the super-network and the sub-architectures, causing instability during the searching process. In this paper, we present a novel approach to fill this gap in image denoising application by connecting model-guided design (MoD) with NAS (MoD-NAS). Specifically, we propose to construct a new search space under a model-guided framework and develop more stable and efficient differential search strategies. MoD-NAS employs a highly reusable width search strategy and a densely connected search block to automatically select the operations of each layer as well as network width and depth via gradient descent. During the search process, the proposed MoD-NAS remains stable because of the smoother search space designed under the model-guided framework. Experimental results on several popular datasets show that our MoD-NAS method has achieved at least comparable even better PSNR performance than current state-of-the-art methods with fewer parameters, fewer flops, and less testing time. “The code associate with this paper is available at: <uri xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>https://see.xidian.edu.cn/faculty/wsdong/Projects/Mod-NAS.htm</uri> ”." @default.
• W4287239779 created "2022-07-25" @default.
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• W4287239779 date "2023-01-01" @default.
• W4287239779 modified "2023-10-16" @default.
• W4287239779 title "Searching Efficient Model-Guided Deep Network for Image Denoising" @default.
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• W4287239779 doi "https://doi.org/10.1109/tip.2022.3231741" @default.
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