Generative Adversarial Networks: Challenges, Solutions, and Evaluation Metrics

The ability of generative adversarial networks (GANs) to learn high-dimensional, complex real data distribution has attracted significant attention and played a significant influence in the Machine Learning (ML) area. In particular, they may easily produce new, pseudo-real, high-quality data samples from latent space without depending on any pre-existing distributional assumptions. With the aid of this potent characteristic, GANs can outperform conventional generative models and can be used for a variety of tasks across numerous domains, including images, text, audio, and video. Its intriguing adversarial learning concept has demonstrated strong promise for re-establishing equilibrium in unbalanced datasets. GANs give a technique to alter the original image in addition to being able to create a fake image. To put it in another way, they can be trained to create any required number of classes (objects, individuals, and identities), and across numerous variations (backgrounds, viewpoints, scale, and light conditions). Our survey offers an in-depth analysis of GAN's benefits, drawbacks, training stability techniques, evaluation measures, and application types.