LAUSR

Abstract Generative adversarial networks (GANs) are one of the most important generative network models. Using real samples, the GAN generates fake samples from the noise given as input to the network. This popular network model, which has recently emerged and consists of several variants, has different applications in many areas. Some of the studies have been implemented by applying GANs to real-world problems. Another part is aimed at improving the performance of GANs or eliminating the disadvantages observed over time. One of these studies is DCGAN. The importance of DCGAN is that it contributes significantly to balancing GAN training with its convolutional architecture. GAN and naturally DCGAN have an unsupervised network structure. While the network is informed that the samples given as input are real or fake, the category label information is not given to the network. In the present study, a method is proposed, which enables creating a supervised network structure when using multi-categories data set with DCGAN structure. The proposed method ensures that noise can be given a category label and this generated category label information can be used in the output layer. This method, which is easily applicable and effective, is named as Supervised DCGAN (SDCGAN).