presentation on Generative Adversarial Network(GAN)

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This overview covers the foundations of Generative Adversarial Networks (GANs), detailing the roles of generators and discriminators, and the dynamics of adversarial training. It explores how GANs learn through their training loop, comparing various models like DCGAN, StyleGAN, and CycleGAN, with practical examples in images and media. Additionally, it addresses the risks associated with GANs, including issues of realism, diversity, and bias, while emphasising the importance of responsible...