Introduction to Generative Adversarial Networks Challenges and Solutions

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Harmeet Khanuja
AARTI AMOD AGARKAR

Abstract


Deep learning has received spectacular adoption in the field of artificial intelligence. With this many deep learning models have been developed. Generative Adversarial Networks (GAN) is one of the deep learning model, which is based on the two-player game from the Game theory. Two neural networks namely; generator and discriminator compete with each other. The proposition of the model variation is to achieve the data distribution through unsupervised learning to generate more realistic data. At present, GANs have been widely studied due to the extensive application prospects which include computer vision like generating lot of data, image to image translation etc. In this paper, the background of the GAN with theoretic model is introduced. Finally the existing research challenges of GANs are discussed with probable solutions giving a wider area of research.

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How to Cite
Khanuja, H., & AGARKAR, A. A. . (2021). Introduction to Generative Adversarial Networks Challenges and Solutions. International Journal of Next-Generation Computing, 12(5). https://doi.org/10.47164/ijngc.v12i5.468

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