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Generative Networks and Royalty-Free Products

Year 2020, , 309 - 324, 30.12.2020

Yasin Özkan , Pakize Erdoğmuş

https://doi.org/10.35377/saucis.03.03.724645
Cited By: 1

Abstract

In recent years, with the increasing power of computers and Graphics Processing Units (GPUs), vast variety of deep neural networks architectures have been created and realized. One of the most interesting and generative type of the networks are Generative Adversarial Networks (GANs). GANs are used to create things such as music, images or a film scenerio. GANs consist of two networks working simultaneously. Generative network captures data distribution and discriminative network estimates the probability of the Generative Network output, coming from training data of discriminative network. The objective is to both maximizing the generative network products reality and minimize the discriminative network classification error. This procedure is a minimax two-player game. In this paper, it has been aimed to review the latest studies with GANs, to gather the recent studies in an article and to discuss the possible issues with royalty free products created by GANs. With this aim, from 2018 to today, the studies on GANs have been gathered to the citation numbers. As a result, the recent studies with GANs have been summarized and the potential issues related to GANs have been submitted.

Keywords

deep learning royalty-free products authentication

References

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Year 2020, , 309 - 324, 30.12.2020

Yasin Özkan , Pakize Erdoğmuş

https://doi.org/10.35377/saucis.03.03.724645
Cited By: 1

Abstract

References

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  • L. He and J. Zhang, "Snowflakes Removal for Single Image Based on Model Pruning and Generative Adversarial Network," 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC), Xiamen, China, pp. 172-176, 2019.
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There are 77 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Articles
Authors

Yasin Özkan 0000-0002-2029-0856

Pakize Erdoğmuş 0000-0003-2172-5767

Publication Date December 30, 2020
Submission Date April 21, 2020
Acceptance Date December 8, 2020
Published in Issue Year 2020

Cite

IEEE Y. Özkan and P. Erdoğmuş, “Generative Networks and Royalty-Free Products”, SAUCIS, vol. 3, no. 3, pp. 309–324, 2020, doi: 10.35377/saucis.03.03.724645.

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