Unleashing the Potential of Generative Adversarial Networks (GANs): An In-Depth Review
Keywords:
Generative Models, Deep Learning, Image Synthesis, Neural Networks, Data Augmentation, Wasserstein DistanceAbstract
Generative Adversarial Networks (GANs) have emerged as one of the most transformative innovations in the field of deep learning, revolutionizing how synthetic data is generated. Introduced by Ian Goodfellow in 2014, GANs consist of two neural networks—the generator and the discriminator—engaged in an adversarial game to improve the generation of realistic data. This review explores the foundational architecture of GANs, their evolution through various advanced models, and their widespread applications across fields such as image and video generation, art, medical imaging, and natural language processing. Despite their remarkable success, GANs face significant challenges, including training instability, mode collapse, and the need for substantial computational resources. We also discuss current efforts to address these challenges and the future potential of GANs, particularly in cross-domain applications and collaboration with other AI techniques. Additionally, ethical concerns related to the misuse of GANs, such as the creation of deepfakes, are considered. This article provides a comprehensive overview of GANs, highlighting both their capabilities and the ongoing research aimed at overcoming their limitations.
References
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