Applications of Generative Adversarial Networks (GANs): An Updated Review

By Hamed Alqahtani, Manolya Kavakli-Thorne, G. Kumar. Archives of Computational Methods in Engineering, 2019

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Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends (2021, background): “There has been a considerable amount of recent research around image generation based on an input image and set of controllable parameters (Lee and Seok, 2017; Alqahtani et al., 2019).”
Generative Adversarial Networks in Time Series: A Systematic Literature Review (2022, background): “In another review, paper [3] the authors give an overview of GAN fundamentals, variants, and applications.”
A comprehensive survey of recent trends in deep learning for digital images augmentation (2021, methodology): “(Alqahtani et al. 2019).”
Synthetic data generation for tabular health records: A systematic review (2022, background): “defined in [14] the basic GAN architecture is composed of two types of neural networks: the generator (G) and the discriminator (D).”
Generative Adversarial Networks for Face Generation: A Survey (2022, background): “Latent space, or the embedding space [4], is the space in which a compressed representation of data lies.”