Theoretical and Natural Science

- The Open Access Proceedings Series for Conferences


Theoretical and Natural Science

Vol. 5, 25 May 2023


Open Access | Article

Supervised Contrastive Generative Adversarial Networks

Honglei Gu * 1
1 The experimental high school attached to Beijing Normal University, Beijing, 100032, China

* Author to whom correspondence should be addressed.

Theoretical and Natural Science, Vol. 5, 234-239
Published 25 May 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Honglei Gu. Supervised Contrastive Generative Adversarial Networks. TNS (2023) Vol. 5: 234-239. DOI: 10.54254/2753-8818/5/20230428.

Abstract

Generative Adversarial Networks (GANs) is becoming more and more popular, artists use them to find their own inspirations, computer scientists use it for data synthesis, workers use it for machine fault diagnosis and so on. However, GANs are flawed despite its popularity: they are unstable. GANs are based on game theory. In a typical GAN model, the generator and the discriminator are both improved by competing with each other. Therefore, in this highly competitive training process, GANs can easily run into trouble while they move towards the optimal solution. In most cases, the case of such instability arises from the loss function, or in other words, the gradient of the loss function. This research proposed a new set of GAN that replaces its objective function with supcon, or the supervised contrastive loss to solve gradient-related problems. We have also proved that under our model, the GANs are less likely to suffer from these two factors of instability. Finally, we have compared our model and the traditional generative adversarial nets.

Keywords

Generative Adversarial Networks, Contrastive learning, 2C loss, Machine Learning.

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2023)
ISBN (Print)
978-1-915371-53-9
ISBN (Online)
978-1-915371-54-6
Published Date
25 May 2023
Series
Theoretical and Natural Science
ISSN (Print)
2753-8818
ISSN (Online)
2753-8826
DOI
10.54254/2753-8818/5/20230428
Copyright
25 May 2023
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated