DWSR: an architecture optimization framework for adaptive super-resolution neural networks based on meta-heuristics

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10256846" target="_blank" >RIV/61989100:27240/24:10256846 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s10462-023-10648-4" target="_blank" >https://link.springer.com/article/10.1007/s10462-023-10648-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10462-023-10648-4" target="_blank" >10.1007/s10462-023-10648-4</a>

Result language

angličtina

Original language name

DWSR: an architecture optimization framework for adaptive super-resolution neural networks based on meta-heuristics

Original language description

Despite recent advancements in super-resolution neural network optimization, a fundamental challenge remains unresolved: as the number of parameters is reduced, the network&apos;s performance significantly deteriorates. This paper presents a novel framework called the Depthwise Separable Convolution Super-Resolution Neural Network Framework (DWSR) for optimizing super-resolution neural network architectures. The depthwise separable convolutions are introduced to reduce the number of parameters and minimize the impact on the performance of the super-resolution neural network. The proposed framework uses the RUNge Kutta optimizer (RUN) variant (MoBRUN) as the search method. MoBRUN is a multi-objective binary version of RUN, which balances multiple objectives when optimizing the neural network architecture. Experimental results on publicly available datasets indicate that the DWSR framework can reduce the number of parameters of the Residual Dense Network (RDN) model by 22.17% while suffering only a minor decrease of 0.018 in Peak Signal-to-Noise Ratio (PSNR), the framework can reduce the number of parameters of the Enhanced SRGAN (ESRGAN) model by 31.45% while losing only 0.08 PSNR. Additionally, the framework can reduce the number of parameters of the HAT model by 5.38% while losing only 0.02 PSNR.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Artificial Intelligence Review

ISSN

0269-2821

e-ISSN

1573-7462

Volume of the periodical

57

Issue of the periodical within the volume

2

Country of publishing house

US - UNITED STATES

Number of pages

21

Pages from-to

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UT code for WoS article

001161054000003

EID of the result in the Scopus database

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