A Review on Domain Adaption and Generative Adversarial Networks(GANs)

In a field of study lik e image classification, where data is of utmost importance, we need to find more reliable methods which can overcome the scarcity of data to produce results comparable to previous benchmark results. In most cases, obtaining labeled data is very difficult b ecause of high cost of human labor and in some cases impossible. The purpose of this paper is to discuss about Domain Adaption and various methods to implement it. The main idea is to use a model trained on a particular dataset to predict on data from a di fferent domain of the same kind, example - model trained on paintings of airplanes predicting on real images of airplanes.