Abstract:
This research explores the performance of different Generative Adversarial
Networks (GANs) on realistic and art images. Specifically, implementation and
comparison of outcomes for Deep Convolutional GANs (DCGANs) and Conditional
GANs and lastly this study evolves to include Info GANs. The methodology involves
training these models on a diverse dataset comprising realistic and art images and
evaluating their performance through various metrics.
This study aims to comprehensively review GAN design for spatial imaging.
By analyzing DCGANs, Conditional GANs, and Info GANs, the research aims to
reveal their strengths and limitations in image generation tasks. Through rigorous
analysis and comparison, we can look at the capabilities and potential applications of
these GAN architectures, contributing to the development of fertility modeling
techniques and their real-world implications
