A Comparative Study of Neural Style Transfer Models
Article No.: 157, Pages 1 - 4
Abstract
The study evaluates various style transfer models and their ability to generate high-quality stylized images. Four distinct style transfer models will be compared and analyzed, taking into consideration their respective strengths and limitations. The outcomes of this investigation are anticipated to provide valuable insights into the effectiveness of these models and facilitate the identification of the optimal approach for achieving superior style transfer in image transformation applications. Additionally, the research aims to contribute to the understanding and tracking of the evolutionary progress of Neural Style transfer methods.
References
[1]
Dongdong Chen, Jing Liao, Lu Yuan, Nenghai Yu, and Gang Hua. 2017. Coherent Online Video Style Transfer. arxiv:1703.09211 [cs.CV]
[2]
Logan Engstrom. 2016. Fast Style Transfer. https://github.com/lengstrom/fast-style-transfer/. commit xxxxxxx.
[3]
Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. 2015. A Neural Algorithm of Artistic Style. arxiv:1508.06576 [cs.CV]
[4]
Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. 2016. Image Style Transfer Using Convolutional Neural Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5]
Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. 2016. Image Style Transfer Using Convolutional Neural Networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2414–2423. https://doi.org/10.1109/CVPR.2016.265
[6]
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Vol. 30. Curran Associates, Inc.https://proceedings.neurips.cc/paper_files/paper/2017/file/8a1d694707eb0fefe65871369074926d-Paper.pdf
[7]
Xun Huang and Serge Belongie. 2017. Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization. arxiv:1703.06868 [cs.CV]
[8]
Justin Johnson. 2015. neural-style. https://github.com/jcjohnson/neural-style.
[9]
Justin Johnson, Alexandre Alahi, and Li Fei-Fei. 2016. Perceptual losses for real-time style transfer and super-resolution. In European Conference on Computer Vision.
[10]
Son Truong Nguyen, Nguyen Quang Tuyen, and Nguyen Hong Phuc. 2021. Deep Feature Rotation for Multimodal Image Style Transfer. In 2021 8th NAFOSTED Conference on Information and Computer Science (NICS). 260–265. https://doi.org/10.1109/NICS54270.2021.9701465
[11]
Manuel Ruder, Alexey Dosovitskiy, and Thomas Brox. 2016. Artistic Style Transfer for Videos. ArXiv abs/1604.08610 (2016).
[12]
Manuel Ruder, Alexey Dosovitskiy, and Thomas Brox. 2016. Artistic Style Transfer for Videos. In Lecture Notes in Computer Science. Springer International Publishing, 26–36. https://doi.org/10.1007/978-3-319-45886-1_3
[13]
Cameron Smith. 2016. neural-style-tf. https://github.com/cysmith/neural-style-tf.
[14]
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2017. Instance Normalization: The Missing Ingredient for Fast Stylization. arxiv:1607.08022 [cs.CV]
[15]
Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13, 4 (2004), 600–612. https://doi.org/10.1109/TIP.2003.819861
[16]
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. 2018. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. arxiv:1801.03924 [cs.CV]
Index Terms
- A Comparative Study of Neural Style Transfer Models
Recommendations
Voxel-Based Three-Dimensional Neural Style Transfer
Advances in Computational IntelligenceAbstractNeural Style Transfer has been successfully applied in the creative process for generating novel artistic 2D images by transferring the style of a painting to an existing content image. These techniques which rely on deep neural networks have been ...
Conditional Fast Style Transfer Network
ICMR '17: Proceedings of the 2017 ACM on International Conference on Multimedia RetrievalIn this paper, we propose a conditional fast neural style transfer network. We extend the network proposed as a fast neural style transfer network by Johnson et al. [1] so that the network can learn multiple styles at the same time. To do that, we add a ...
Automatic semantic style transfer using deep convolutional neural networks and soft masks
AbstractThis paper presents an automatic image synthesis method to transfer the style of an example image to a content image. When standard neural style transfer approaches are used, the textures and colours in different semantic regions of the style ...
Comments
Information & Contributors
Information
Published In
November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 May 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 29Total Downloads
- Downloads (Last 12 months)29
- Downloads (Last 6 weeks)4
Reflects downloads up to 08 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format