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This article aims to explore and research GANs as a tool for mobile devices that can generate high-resolution images from low-resolution samples and reduce blurring. In addition, the authors also analyse the specifics of GAN, SRGAN, and ESRGAN loss functions and their features. GANs are widely used for a vast range of applied tasks for image manipulations. They’re able to synthesize, combine, and restore graphical samples of high quality that are almost indistinguishable from real data. The main scope of the research is to study the possibility to use GANs for the said tasks, and their potential implementation in mobile applications.

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The monograph examines the prerequisites and scientific foundations for creation of the Strategy for Artificial Intelligence Development in Ukraine as well as means and ways of its effective implementation. For specialists, postgraduate, and graduate students in the field of artificial intelligence, information technologies, philosophy, state formation, and economics

Cross-domain artificial intelligence (AI) frameworks are the keys to amplify progress in science. Cutting edge deep learning methods offer novel opportunities for retrieving, optimizing, and improving different data types. AI techniques provide new ways for enhancing and polishing existing models that are used in applied sciences. New breakthroughs in generative adversarial neural networks (GANNs/GANs) and deep learning allow to drastically increase the quality of diverse graphic samples obtained with research equipment. All these innovative approaches can be compounded into a unified academic and technological pipeline that can radically elevate and accelerate scientific research and development. The authors analyze a number of successful cases of GAN and deep learning applications in applied scientific fields (including observational astronomy, health care, materials science, deep fakes, bioinformatics, and typography) and discuss advanced approaches for increasing GAN and DL efficiency in terms of performance calibration using modified data samples, algorithmic enhancements, and various hybrid methods of optimization.