Generative Adversarial Networks for Classification

Steven A. Israel, J. H. Goldstein, Jeffrey S. Klein, James Talamonti, Franklin Tanner, Shane Zabel, Philip A. Sallee, Lisa McCoy

Abstract: Our team is reviewing tools and techniques that enable rapid prototyping. Generative Adversarial Networks (GANs) have been shown to reduce training requirements for detection problems. GANs compete generative and discriminative classifiers to improve detection performance. This paper expands the use of GANs from detection (k=2) to classification (k>2) problems. Several GAN network structures and training set sizes were compared to the baseline discriminative network and Bayes' classifiers. The results show no significant performance differences among any of the network configurations or training set size trials. However, the GANs trained with fewer network nodes and iterations than needed by the discriminator classifiers alone.