Generative Adversarial Networks (GANs) have revolutionized the field of generative modeling. This topic delves into the architecture and training process of GANs, explaining how they can generate highly realistic and diverse synthetic data. It explores various GAN architectures and their applications in image generation, style transfer, video synthesis, and other creative domains. Challenges and limitations of GANs are also discussed.
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