References
[1]. Zhang G Chen J L Song J et al 2020 Chinese Landscape Painting Automatic Generation Model Based on Adversarial Generation Network (in Chinese), Phase 3 Computer and Telecommunications p 6
[2]. Zhao J Li F F 2023 A GAN-based Lightweight ink Painting Style Transfer Model (in Chinese) Volume 36 Issue 2 Electronic Science and Technology p 6
[3]. Niemitalo O 2010 A method for training artificial neural networks to generate missing data within a variable context Internet Archive (Wayback Machine). Archived from the original on March 12 2012 Retrieved February 22 2019
[4]. Goodfellow I and Pouget-Abadie J and Mirza M et al 2014 Generative Adversarial Nets (Massachusetts:MIT Press/Neural Information Processing Systems)
[5]. Zhu J Y and Park T and Isola P et al 2017 Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (IEEE)
[6]. Isola P and Zhu J Y and Zhou T et al 2016 Image-to-Image Translation with Conditional Adversarial Networks Proceedings of the IEEE conference on computer vision and pattern recognition pp1125-1134 He B and Feng G and Ma D et al 2018 ChipGAN: A Generative Adversarial Network for Chinese Ink Wash Painting Style Transfer (ACM/Multimedia Conference)
[7]. Xue A 2020 End-to-End Chinese Landscape Painting Creation Using Generative Adversarial Networks Proceedings of the IEEE/CVF Winter conference on applications of computer vision pp 3863-3871
[8]. Google 2022 Introduction | Machine Learning | Google Developers https://developers.google.com/machine-learning/gan#:~:text=Generative%20adversarial%20networks%20(GANs)%20are,belong%20to%20any%20real%20person
[9]. He K and Zhang X and Ren S et al 2016 Deep Residual Learning for Image Recognition (IEEE)
Cite this article
Jin,Y.;Li,Z.;Lu,T. (2023). The Style Transfer of Photos and Landscape Paintings Based on CycleGAN Combined with Nested Edge Detection Module. Applied and Computational Engineering,8,153-161.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).
References
[1]. Zhang G Chen J L Song J et al 2020 Chinese Landscape Painting Automatic Generation Model Based on Adversarial Generation Network (in Chinese), Phase 3 Computer and Telecommunications p 6
[2]. Zhao J Li F F 2023 A GAN-based Lightweight ink Painting Style Transfer Model (in Chinese) Volume 36 Issue 2 Electronic Science and Technology p 6
[3]. Niemitalo O 2010 A method for training artificial neural networks to generate missing data within a variable context Internet Archive (Wayback Machine). Archived from the original on March 12 2012 Retrieved February 22 2019
[4]. Goodfellow I and Pouget-Abadie J and Mirza M et al 2014 Generative Adversarial Nets (Massachusetts:MIT Press/Neural Information Processing Systems)
[5]. Zhu J Y and Park T and Isola P et al 2017 Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (IEEE)
[6]. Isola P and Zhu J Y and Zhou T et al 2016 Image-to-Image Translation with Conditional Adversarial Networks Proceedings of the IEEE conference on computer vision and pattern recognition pp1125-1134 He B and Feng G and Ma D et al 2018 ChipGAN: A Generative Adversarial Network for Chinese Ink Wash Painting Style Transfer (ACM/Multimedia Conference)
[7]. Xue A 2020 End-to-End Chinese Landscape Painting Creation Using Generative Adversarial Networks Proceedings of the IEEE/CVF Winter conference on applications of computer vision pp 3863-3871
[8]. Google 2022 Introduction | Machine Learning | Google Developers https://developers.google.com/machine-learning/gan#:~:text=Generative%20adversarial%20networks%20(GANs)%20are,belong%20to%20any%20real%20person
[9]. He K and Zhang X and Ren S et al 2016 Deep Residual Learning for Image Recognition (IEEE)