generative adversarial network

The Generative Warfare network (GAN, generative adversarial Networks) is a deep learning model, which is one of the most promising methods for unsupervised learning in the complex distribution. The model produces quite good output through the mutual game Learning of the framework (at least) two modules: the generation

training data, and proves that there is a large disturbance in the final output layer caused by the disturbance of small input space . This method is similar to the difficult-to-mistake sample mining (hard-negative mining), that is, the collection of classifier error large samples, so that the input distribution of further iterations to focus on the distribution of these difficult samples. By minimizing the distortion function d, such as the following formula, to obtain

Thesis: Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing Systems. 2014. The two-person zero-sum game in Gan-inspired game theory, pioneered by [Goodfellow et al, NIPS 2014], contains a generative model (generative) and a discriminant model (discriminative models D). The