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- Publisher Website: 10.1109/TVCG.2019.2921336
- Scopus: eid_2-s2.0-85092680398
- PMID: 31180860
- WOS: WOS:000574745100018
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Article: Neural Style Transfer: A Review
Title | Neural Style Transfer: A Review |
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Authors | |
Keywords | Neural style transfer (NST) convolutional neural network (CNN) |
Issue Date | 2020 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=2945 |
Citation | IEEE Transactions on Visualization and Computer Graphics, 2020, v. 26 n. 11, p. 3365-3385 How to Cite? |
Abstract | The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. In this paper, we aim to provide a comprehensive overview of the current progress towards NST. We first propose a taxonomy of current algorithms in the field of NST. Then, we present several evaluation methods and compare different NST algorithms both qualitatively and quantitatively. The review concludes with a discussion of various applications of NST and open problems for future research. A list of papers discussed in this review, corresponding codes, pre-trained models and more comparison results are publicly available at: https://osf.io/f8tu4/. |
Persistent Identifier | http://hdl.handle.net/10722/301335 |
ISSN | 2023 Impact Factor: 4.7 2023 SCImago Journal Rankings: 2.056 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Jing, Y | - |
dc.contributor.author | Yang, Y | - |
dc.contributor.author | Feng, Z | - |
dc.contributor.author | Ye, J | - |
dc.contributor.author | Yu, Y | - |
dc.contributor.author | Song, M | - |
dc.date.accessioned | 2021-07-27T08:09:34Z | - |
dc.date.available | 2021-07-27T08:09:34Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE Transactions on Visualization and Computer Graphics, 2020, v. 26 n. 11, p. 3365-3385 | - |
dc.identifier.issn | 1077-2626 | - |
dc.identifier.uri | http://hdl.handle.net/10722/301335 | - |
dc.description.abstract | The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. In this paper, we aim to provide a comprehensive overview of the current progress towards NST. We first propose a taxonomy of current algorithms in the field of NST. Then, we present several evaluation methods and compare different NST algorithms both qualitatively and quantitatively. The review concludes with a discussion of various applications of NST and open problems for future research. A list of papers discussed in this review, corresponding codes, pre-trained models and more comparison results are publicly available at: https://osf.io/f8tu4/. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=2945 | - |
dc.relation.ispartof | IEEE Transactions on Visualization and Computer Graphics | - |
dc.rights | IEEE Transactions on Visualization and Computer Graphics. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Neural style transfer (NST) | - |
dc.subject | convolutional neural network (CNN) | - |
dc.title | Neural Style Transfer: A Review | - |
dc.type | Article | - |
dc.identifier.email | Yu, Y: yzyu@cs.hku.hk | - |
dc.identifier.authority | Yu, Y=rp01415 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1109/TVCG.2019.2921336 | - |
dc.identifier.pmid | 31180860 | - |
dc.identifier.scopus | eid_2-s2.0-85092680398 | - |
dc.identifier.hkuros | 323531 | - |
dc.identifier.volume | 26 | - |
dc.identifier.issue | 11 | - |
dc.identifier.spage | 3365 | - |
dc.identifier.epage | 3385 | - |
dc.identifier.isi | WOS:000574745100018 | - |
dc.publisher.place | United States | - |