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@article{164347,
        author = {Dr. T. Amalraj Victoire and M. Vasuki and John Paul S},
        title = {ADVANCING AI CREATIVITY: AN EXPLORATION OF GENERATIVE ADVERSARIAL NETWORKS AND THEIR IMPACT},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {595-603},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164347},
        abstract = {Generative Adversarial Networks (GANs) have emerged as a powerful framework for fostering AI creativity across various domains, including image synthesis, music generation, and text generation. This paper provides a comprehensive exploration of GANs and their impact on advancing AI creativity. Beginning with the foundational work of Goodfellow et al. (2014),to trace the evolution of GANs, highlighting key milestones such as DCGANs, conditional GANs, and progressive growing techniques. To discuss how GANs have revolutionized creative content generation by enabling tasks such as image-to-image translation, style transfer, and artistic synthesis. Furthermore, to delve into the application of GANs in non-visual domains, including music and text generation, showcasing their versatility and potential for fostering creativity beyond traditional mediums. Additionally, to examine the societal implications of AI-generated content, including considerations of authenticity, ethics, and responsible use. Through a synthesis of recent research and advancements, this paper aims to provide insights into the role of GANs in shaping the future of AI creativity and inspire rapidly evolving field.},
        keywords = {Generative Adversarial Networks (GANs), AI creativity, Image synthesis, Music generation, Text generation, Style transfer, Image-to-image translation, Creative content generation, Artistic synthesis, Ethical considerations, Responsible AI.},
        month = {},
        }