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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM27349
Volume:
7 |
12 |
Dec 2023
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Neural Style Transfer (NST) has emerged as a captivating domain in the intersection of deep learning and computer vision. This project delves into the comparative analysis of two prominent neural network architectures, VGG and ResNet, within the context of NST. The study aims to explore the efficacy of each architecture in preserving the content of an image while transferring the artistic style of another. The project’s objectives include the implementation of NST using both VGG and ResNet, with a meticulous evaluation of their performance based on visual outputs.
KEYWORDS
Publication Date
12/19/2023
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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