Generative Adversarial Neural Networks and Deep Learning: Successful Cases and Advanced Approaches

Authors

  • Oleksandr Striuk
  • Yuriy Kondratenko

DOI:

https://doi.org/10.47839/ijc.20.3.2278

Keywords:

generative adversarial network, neural networks, deep learning, machine learning, artificial intelligence

Abstract

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.

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Published

2021-09-30

How to Cite

Striuk, O., & Kondratenko, Y. (2021). Generative Adversarial Neural Networks and Deep Learning: Successful Cases and Advanced Approaches. International Journal of Computing, 20(3), 339-349. https://doi.org/10.47839/ijc.20.3.2278

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