Review and experimental comparison of generative adversarial networks for synthetic image generation

    Gabriela Vdoviak Affiliation
    ; Henrikas Giedra Affiliation


The application of machine learning algorithms has become widespread particularly in fields such as medicine, business, and commerce. However, achieving accurate classification results with these algorithms often relies on large-scale training datasets, making data collection a lengthy and complex process. This paper reviews the current utilization of generative adversarial network (GAN) architectures and discusses recent scientific research on their practical applications. The study emphasizes the significance of addressing data scarcity in the process of training the machine learning algorithms and highlights the potential of advanced GAN architectures, in particular StyleGAN2-ADA, to mitigate this challenge. The findings contribute to ongoing efforts aimed at enhancing the efficiency and applicability of artificial intelligence across diverse domains by presenting a viable solution to the constraint of limited training data for image classification tasks.

Keyword : computer vision, convolutional neural networks, deep learning, generative adversarial networks, image classification, image synthesis

How to Cite
Vdoviak, G., & Giedra, H. (2024). Review and experimental comparison of generative adversarial networks for synthetic image generation. New Trends in Computer Sciences, 2(1), 1–18.
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May 30, 2024
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