Neural Architecture Search for Deep Image Prior
Kary Ho[1], Andrew Gilbert[1], Hailin Jin[2], John Collomosse[1,2]
[1] Centre for Vision Speech and Signal Processing, University of Surrey
[2] Creative Intelligence Lab, Adobe Research
[1] Centre for Vision Speech and Signal Processing, University of Surrey
[2] Creative Intelligence Lab, Adobe Research
Abstract
We present a neural architecture search (NAS) technique to enhance the performance of image de-noising, in-painting, and super-resolution tasks 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.
Paper
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Citation
@INPROCEEDINGS{Ho20,
title = {Neural Architecture Search for Deep Image Prior},
year={2020},
booktitle={arXiv preprint arXiv:2001.04776},
author={Ho, K. and Gilbert, A. and Jin, H. and Collomosse, J.}
}
title = {Neural Architecture Search for Deep Image Prior},
year={2020},
booktitle={arXiv preprint arXiv:2001.04776},
author={Ho, K. and Gilbert, A. and Jin, H. and Collomosse, J.}
}