Generative adversarial networks (GANs) in machine learning: Applications and challenges

Abstract

Generative Adversarial Networks (GANs) have emerged as a revolutionary paradigm in machine learning, demonstrating remarkable capabilities in generating realistic data across various domains. This review paper provides a comprehensive analysis of the diverse applications and inherent challenges associated with GANs, highlighting their pivotal role in shaping the landscape of artificial intelligence. The applications of GANs span a multitude of domains, including image synthesis, style transfer, and data augmentation. In the realm of computer vision, GANs have demonstrated unprecedented success in generating high-fidelity images that are indistinguishable from real photographs. Moreover, GANs have proven instrumental in the field of image-to-image translation, enabling the transformation of images across different domains, such as turning day scenes into night or transforming satellite imagery to maps. In addition to image-centric applications, GANs have found utility in the generation of realistic text, audio, and even 3D objects, showcasing their versatility across various modalities. Despite their remarkable achievements, GANs also present a set of challenges that necessitate careful consideration. The notorious mode collapse phenomenon, where the generator produces limited diversity in generated samples, remains a significant hurdle. Mitigating this issue requires ongoing research to enhance the stability and diversity of GANs. Ethical concerns related to the malicious use of generated content and the potential for bias in training data also warrant attention. Additionally, GANs exhibit sensitivity to hyperparameters and are prone to training instability, necessitating rigorous tuning and exploration of novel training methodologies.