A Review of Generative Adversarial Networks and its Applications

Abstract

The Generative Adversarial Networks are specific type of Artificial Intelligence Algorithms which are designed to solve problems related with Generative Modelling. Generative Models which are based upon Deep Learning are usually used when designing applications based on these models but Generative Adversarial Networks are very popular among the scientists who work on Computer Vision based algorithms because of their capability to produce high resolution videos and images. The Conventional Artificial Intelligence has been emerged into the new field Generative Artificial Intelligence and there is an indispensable need to investigate upon the novel algorithms which belong to the Generative Artificial Intelligence field, therefore Generative Adversarial Networks are one of these algorithms. The Generator and Discriminator are the principal components of Generative Adversarial Networks, these components are Neural Networks and both work in conjunction with each other. The output of the Generator is connected with the input of the Discriminator. The Discriminator plays an important role in distinguishing between the real data instances and fake data instances produced by the Generator. The Generator and Discriminator functions asynchronously with each other. When the Discriminator is in the phase of training, the Generator is not trained and the weights associated with data remain static during the training phase of the Discriminator. The Generator provides the training data to the Discriminator and completes its training on the acquired data from the Generator. The indispensable need of Generative Adversarial Networks today make it significant for researchers to study and apply them in the field of Computer Vision, Information Security Cyber Crimes Detection. From the review process it has been observed that Generative Adversarial Networks are very important and exciting innovative processes and algorithms which are now widely used in the field of Machine Learning and its applications.