Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci