Study on Generative Adversarial Network in Discrete Data: A Survey

Generative Adversarial Networks (GANs) have emerged as a pivotal research focus within artificial intelligence due to their exceptional capabilities in data generation. Their ability to produce high-quality synthetic data has garnered significant attention, leading to their application in diverse domains such as image and video generation, classification, and style transfer. Beyond these continuous data applications, GANs are also being leveraged for discrete data tasks, including text and music generation. The distinct nature of continuous and discrete data poses unique challenges for GANs. In particular, generating discrete values necessitates the use of Policy Gradient algorithms from reinforcement learning to avoid the direct back-propagation typically used for continuous values. The generator must map latent variables into discrete domains, and unlike continuous value generation, this process involves subtle adjustments to the generator’s outputs to progressively align with real discrete data, guided by the discriminator. This paper aims to provide a thorough review of GAN architectures, fundamental concepts, and applications in the context of discrete data. Additionally, it addresses the existing challenges, evaluation metrics, and future research directions in this burgeoning field.