Data generation using generative adversarial networks to increase data volume
Aitimova U. Aitimov M. Mukhametzhanova B. Issakulova Z. Kassymova A. Ismailova A. Kadirkulov K. Zhumabayeva A.
April 2024Institute of Advanced Engineering and Science
International Journal of Electrical and Computer Engineering
2024#14Issue 22369 - 2376 pp.
The article is an in-depth analysis of two leading approaches in the field of generative modeling: generative adversarial networks (GANs) and the pixel-to-pixel (Pix2Pix) image translation model. Given the growing interest in automation and improved image processing, the authors focus on the key operating principles of each model, analyzing their unique characteristics and features. The article also explores in detail the various applications of these approaches, highlighting their impact on modern research in computer vision and artificial intelligence. The purpose of the study is to provide readers with a scientific understanding of the effectiveness and potential of each of the models, and to highlight the opportunities and limitations of their application. The authors strive not only to cover the technical aspects of the models, but also to provide a broad overview of their impact on various industries, including medicine, the arts, and solving real-world problems in image processing. In addition, we have identified prospects for the use of these technologies in various fields, such as medicine, design, art, entertainment, and in unmanned aerial vehicle systems. The ability of GANs and Pix2Pix to adapt to a variety of tasks and produce high-quality results opens up broad prospects for industry and research.
Deep learning , Discriminator , Generative adversarial , Image processing , networks , Pixel-to-pixel
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Department of Information Systems, S. Seifullin Kazakh AgroTechnical Research University, Astana, Kazakhstan
Kyzylorda Regional Branch, The Academy of Public Administration under the President of the Republic of Kazakhstan, Kyzylorda, Kazakhstan
Department of Information and Computing System, Abylkas Saginov Karaganda Technical University, Karaganda, Kazakhstan
Department of Automation and Telecommunications, M.H. Dulaty Taraz Regional University, Taraz, Kazakhstan
Department of Information Technology, Zhangir Khan University, Uralsk, Kazakhstan
Department of Space Engineering and Technology, Faculty of Physics and Technology, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
Department of Information Systems
Kyzylorda Regional Branch
Department of Information and Computing System
Department of Automation and Telecommunications
Department of Information Technology
Department of Space Engineering and Technology
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