Generative classifier pix2pix

Abstract

Generative adversarial networks have been widely developed to generate new data, and they have been used for several different applications. Some networks have been developed to classify data at the discriminator level, either by modifying the loss function or by adding a classifier. In this paper, the generative classifier pix2pix, a classifier based on generative adversarial networks, specifically the pix2pix, has been introduced. The classification is done without the need to keep the discriminator or to add additional networks, only the generator is used to classify the data. This classification requires the preparation of a reference dataset. The generative classifier pix2pix was applied to the GC character recognition task using 50000 images for training, it achieved 99.36%. It was also applied to the ORL face recognition task using 360 images for training, and it achieved an average of 97.99%.