Recently, with the expansion of the Generative Adversarial Network (GAN) in deep learning, it has been found that using discriminators leads to a network with higher performance [2]. GAN is composed of two networks: a generator and a discriminator. The discriminator is trained to determine whether the input data distribution is close to the ground-truth data distribution or the generated data distribution. At the same time, the generator is trained to fool the discriminator, by generating more accurate data. Motivated