A Survey of Generative Adversarial Networks
Abstract
Generative adversarial networks(GANs) coming from the game theory allow machines to learn deep representations without extra training data. By training two adversarial networks, including a generator and a discriminator, GANs could get the distribution of the real samples. This capability makes it a prospect learning method in image synthesis, image recognition, image translation etc. In this paper, we survey the state of the art of GANs by categorizing the GANs into four classifications on the basis of GANs' functions and list two application domains: vision computing & natural language processing(NLP) regarding to GANs' applications.
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