MAGAN: A Meta-Analysis for Generative Adversarial Networks' Latent Space

Frederic Rizk, Rodrigue Rizk, Dominick Rizk, Chee-Hung Henry Chu

Abstract: Generative Adversarial Networks (GANs) are an emerging class of deep neural networks that has sparked considerable interest in the field of unsupervised learning because of its exceptional data generation performance. Nevertheless, the GAN’s latent space that represents the core of these generative models has not been studied in depth in terms of its effect on the generated image space. In this paper, we propose and evaluate MAGAN, an algorithm for Meta-Analysis for GANs’ latent space. GAN-derived synthetic images are also evaluated in terms of their efficiency in complementing the data training, where the produced output is employed for data augmentation, mitigating the labeled data scarcity. The results suggest that GANs may be used as a parameter-controlled data generator for data-driven augmentation. The quantitative findings show that MAGAN can correctly trace the relationship between the arithmetic adjustments in the latent space and their effects on the output in the im

External IDs:dblp:conf/icpram/RizkRRC23