Generative Adversarial Networks (GANs): Advancements, Challenges, and Future Prospects
Keywords:
Generative Adversarial Networks, GANs, Deep Learning, Image Synthesis, Mode Collapse, Adversarial Training, Neural Networks, Artificial Intelligence, Innovations, ChallengesAbstract
Generative Adversarial Networks (GANs) have revolutionized the field of artificial intelligence by enabling the generation of high-quality synthetic data. Introduced by Ian Good fellow in 2014, GANs consist of two neural networks, a generator and a discriminator, which engage in a minimax game to improve their respective functionalities. The generator creates synthetic data resembling real-world data, while the discriminator evaluates the authenticity of this data. This adversarial process has led to breakthroughs in image synthesis, text generation, and audio production. However, despite their remarkable potential, GANs face several challenges, including training instability, mode collapse, and ethical concerns related to data misuse. This paper explores recent innovations in GAN architectures, their applications across various domains, and the associated challenges. It provides a comprehensive review of state-of-the-art techniques designed to enhance GAN performance and stability. Finally, the paper discusses ethical considerations and future research directions essential for advancing GAN technology.
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