Neural Architecture Search for Deep Image Prior
We present a neural architecture search (NAS) technique to enhance the performance of unsupervised image de noising, in painting and super resolution under the recently proposed Deep Image Prior (DIP) We show that evolutionary search can automatically optimize the encoder decoder (E D) structure and meta parameters of the DIP network, which serves as a content specific prior to regularize these single image restoration tasks. Our binary representation encodes the design space for an asymmetric E D network that typically converges to yield a content specific DIP within 10 20 generations using a population size of 500. The optimized architectures consistently improve upon the visual quality of classical DIP for a diverse range of photographic and artistic content.
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