GANs for Improving Models in Genomics

One of the significant developments in the field of Deep learning (DL) has been the introduction of new generative models. The most popular generative models are Generative Adversarial Networks (GANs), Variational Autoencoders (VAE), deep autoregressive models, style transfer, and so on. We learned about what VAEs are in the previous chapter. GANs have become a hot topic in the DL research community in the last few years. They were introduced by Ian Goodfellow in 2014 and are considered one of the most interesting ideas of the last 10 years by Yann LeCun, who is considered the father of modern DL. A GAN, as the name suggests, is a type of generative model that is trained in an adversarial setting to learn data distribution that is closer to the real world, thereby generating synthetic data inexpensively. GANs have revolutionized many domains such as natural language processing (NLP), computer vision (CV), and, most recently, genomics because of...