Reviewing Generative Adversarial Networks: Advances, Challenges, and Applications
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Abstract
Generative adversarial networks (GANs) have emerged as powerful tools for generating realistic data samples across various domains, including image synthesis, text generation, and video generation. This review paper offers a comprehensive survey of GAN architectures, training techniques, and applications. It discusses recent advancements, challenges, and future research directions in the field of generative modeling with GANs.
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