Generative Adversarial Networks

A generative adversarial network is a subclass of machine learning frameworks in which when we give a training set, this technique learns to generate new data with the same statistics as the training set with the help of algorithmic architectures that uses two neural networks to generate new, synthetic instances of data that is very much similar to the real data and GANs were designed in 2014 by Ian Goodfellow and his colleagues. GANs are usually trained to generate images from random noises and a GAN has usually two parts in which it works namely the Generator that generates new samples of images and the second is a Discriminator that classifies images as real or fake for example we can train a GAN model to generate digit images that look like hand-written digit images from the MNIST dataset and apart from this GANs are widely used for voice generation, image generation or video generation. There are a variety of reasons why fans are so exciting and one of them is because GANs were the first generative algorithms to give convincingly good results also they have opened up many new directions for research and GANs themselves is considered to be the most prominent research in machine learning in the last several years, and since then GANs have started a revolution in deep learning and this revolution has produced some major technological breakthroughs in the history of computer science and artificial intelligence. From the perspective of AI researchers, this was a breakthrough. Generative adversarial networks (GANs) have been improved over the years and despite all the hurdles brought by this past decade of research, GANs have generated content that will become increasingly difficult to distinguish from real content and comparing image generation in 2014 to today, the quality was not expected to become that good and if the progress continues like this, GANs will remain a very important research project in future provided the acceptance of GANs and their applications by the research community.