THE USE OF GENERATIVE-ADVERSARIAL NEURAL NETWORKS FOR IMAGE GENERATION
This article discusses the topic of image generation using neural networks. Thanks to the development of deeper learning and artificial intelligence, neural networks have become a powerful tool for creating realistic and expressive images. Image generation using neural networks is one of the most promising areas of artificial intelligence. Neural networks allow you to generate images that not only meet certain requirements, but are also new and original. This article discusses the key aspects of using neural networks in image generation. The main attention is paid to the analysis of various architectures and approaches in the field of image generation using neural networks. Key aspects such as conditional generation, generative-adversarial networks (GAN) are investigated and compared. Applications of neural networks in various fields, including art, design and synthesis of photorealistic images, are also considered. The most well-known neural networks used to solve this problem are presented, as well as their advantages and disadvantages. The prospects for the development of neural networks for image generation are discussed.
Ilyinskaya E.V., Golysheva E.N., Medvedev A.A., Masalitin N.S. The use of generative-adversarial neural networks for image generation // Research result. Information technologies. – Т.9, №1, 2024. – P. 73-78. DOI: 10.18413/2518-1092-2024-9-1-0-8
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