Neural architecture search for deep image prior
نویسندگان
چکیده
We present a neural architecture search (NAS) technique to enhance image denoising, inpainting, and super-resolution tasks under the recently proposed Deep Image Prior (DIP). show that evolutionary can automatically optimize encoder-decoder (E-D) structure meta-parameters of DIP network, which serves as content-specific prior regularize these single restoration tasks. Our binary representation encodes design space for an asymmetric E-D network typically converges yield within 10--20 generations using population size 500. The optimized architectures consistently improve upon visual quality classical diverse range photographic artistic content.
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ژورنال
عنوان ژورنال: Computers & Graphics
سال: 2021
ISSN: ['0097-8493', '1873-7684']
DOI: https://doi.org/10.1016/j.cag.2021.05.013