Combine model fine-tuning freezing layers and adaptive filter modulation to implement transfer learning for GANs

Research Article
Open access

Combine model fine-tuning freezing layers and adaptive filter modulation to implement transfer learning for GANs

Chen Yang 1*
  • 1 Cardiff University    
  • *corresponding author rickyyang1997@163.com
Published on 11 December 2023 | https://doi.org/10.54254/2755-2721/27/20230189
ACE Vol.27
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-199-5
ISBN (Online): 978-1-83558-200-8

Abstract

Generative Adversarial Network (GAN) requires more resources to train than other deep learning models and its loss function converges more slowly. For this reason, scholars at home and abroad have proposed a GANS algorithm based on transfer learning, which is applied to fewer samples, thus improving the training effect of GANS algorithm. In this paper, we provide a new way to perform the transfer of genetic algorithms and combine the two ways. On this basis, we will compare and analyze a variety of transfer learning algorithms to verify the feasibility and effectiveness of the joint application.

Keywords:

Yang,C. (2023). Combine model fine-tuning freezing layers and adaptive filter modulation to implement transfer learning for GANs. Applied and Computational Engineering,27,108-112.
Export citation

References

[1]. Goodfellow,I.J. et al. (2014) Generative adversarial nets. Advances in neural information processing systems, 27:2-9.

[2]. Arjovsky,M., Chintala,S., Bottou,L., (2017) Wasserstein GAN. arXiv preprint. arXiv:1701.07875.

[3]. Wang,Y.X. et al. (2018) Transferring GANs: generating images from limited data. In: ECCV, pp. 2-17.

[4]. Zhao,W. (2017) Research on the deep learning of the small sample data based on transfer learning. In: AIP Conference Proceedings.

[5]. Fr´egier, Y., Gouray, J.B. (2019) Mind2Mind : transfer learning for GANs. arXiv preprint arXiv:1906.11613v1.

[6]. Radford, A., Metz, L., Chintala, S. (2016) Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv preprint arXiv:1511.06434

[7]. KAIST, S.M., POSTECH, M.C., KAIST, J.S. (2020) Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs. arXiv preprint arXiv:2002.10964v2.

[8]. Zhao, M.Z., Cong, Y.L., Carin, L. (2020) On Leveraging Pretrained GANs for Generation with Limited Data. arXiv preprint arXiv:2002.11810v3.

[9]. Gwylab. (2019) Download and introduction of the data set. http://www.seeprettyface.com/mydataset.html.

[10]. Maximilian, S. (2020) pytorch-fid: FID Score for PyTorch.Version 0.1.1. https://github.com/mseitzer/pytorch-fid.

[11]. Xu, Q.T. et al. (2018) An empirical study on evaluation metrics of generative adversarial networks. arXiv preprint arXiv:1806.07755v2.

Cite this article

Yang,C. (2023). Combine model fine-tuning freezing layers and adaptive filter modulation to implement transfer learning for GANs. Applied and Computational Engineering,27,108-112.

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

ISBN：978-1-83558-199-5(Print) / 978-1-83558-200-8(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.27
ISSN：2755-2721(Print) / 2755-273X(Online)

© 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]. Goodfellow,I.J. et al. (2014) Generative adversarial nets. Advances in neural information processing systems, 27:2-9.

[2]. Arjovsky,M., Chintala,S., Bottou,L., (2017) Wasserstein GAN. arXiv preprint. arXiv:1701.07875.

[3]. Wang,Y.X. et al. (2018) Transferring GANs: generating images from limited data. In: ECCV, pp. 2-17.

[4]. Zhao,W. (2017) Research on the deep learning of the small sample data based on transfer learning. In: AIP Conference Proceedings.

[5]. Fr´egier, Y., Gouray, J.B. (2019) Mind2Mind : transfer learning for GANs. arXiv preprint arXiv:1906.11613v1.

[6]. Radford, A., Metz, L., Chintala, S. (2016) Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv preprint arXiv:1511.06434

[7]. KAIST, S.M., POSTECH, M.C., KAIST, J.S. (2020) Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs. arXiv preprint arXiv:2002.10964v2.

[8]. Zhao, M.Z., Cong, Y.L., Carin, L. (2020) On Leveraging Pretrained GANs for Generation with Limited Data. arXiv preprint arXiv:2002.11810v3.

[9]. Gwylab. (2019) Download and introduction of the data set. http://www.seeprettyface.com/mydataset.html.

[10]. Maximilian, S. (2020) pytorch-fid: FID Score for PyTorch.Version 0.1.1. https://github.com/mseitzer/pytorch-fid.

[11]. Xu, Q.T. et al. (2018) An empirical study on evaluation metrics of generative adversarial networks. arXiv preprint arXiv:1806.07755v2.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.