Abstract:Generative adversarial networks (GANs) are a powerful deep learning model for synthesizing realistic images; however, they can be difficult to train and are prone to instability and mode collapse. This paper presents a modified deep learning model called Identity Generative Adversarial Network (IGAN) to address the challenges of training and instability faced by generative adversarial models in synthesizing realistic images. The IGAN model includes three modifications to improve the performance of DCGAN: a non… Show more
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