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• W3035122910 abstract "Recent advancements in Neural Architecture Search (NAS) resulted in finding new state-of-the-art Artificial Neural Network (ANN) solutions for tasks like image classification, object detection, or semantic segmentation without substantial human supervision. In this paper, we focus on exploring NAS for a dense prediction task that is image denoising. Due to a costly training procedure, most NAS solutions for image enhancement rely on reinforcement learning or evolutionary algorithm exploration, which usually take weeks (or even months) to train. Therefore, we introduce a new efficient implementation of various superkernel techniques that enable fast (6-8 RTX2080 GPU hours) single-shot training of models for dense predictions. We demonstrate the effectiveness of our method on the SIDD+ benchmark for image denoising [3]." @default.
• W3035122910 created "2020-06-19" @default.
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• W3035122910 date "2020-06-01" @default.
• W3035122910 modified "2023-09-26" @default.
• W3035122910 title "Superkernel Neural Architecture Search for Image Denoising" @default.
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• W3035122910 doi "https://doi.org/10.1109/cvprw50498.2020.00250" @default.
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