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GAGAN: Enhancing Image Generation Through Hybrid Optimization of Genetic Algorithms and Deep Convolutional Generative Adversarial Networks

Generative Adversarial Networks (GANs) are highly effective for generating realistic images, yet their training can be unstable due to challenges such as mode collapse and oscillatory convergence. In this paper, we propose a novel hybrid optimization method that integrates Genetic Algorithms (GAs) t...

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Hoofdauteurs: Despoina Konstantopoulou, Paraskevi Zacharia, Michail Papoutsidakis, Helen C. Leligou, Charalampos Patrikakis
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: MDPI AG 2024-12-01
Reeks:Algorithms
Onderwerpen:
image generation
generative AI
genetic algorithms
deep convolutional generative adversarial networks
discriminator weight optimization
Online toegang:https://www.mdpi.com/1999-4893/17/12/584
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