As the first successful general purpose way of generating new data, GANs have shown great potential for a wide range of practical applications (including those in the fields of art, fashion, medicine and finance). It is one of the most popular research topics of recent times. GANs are the new class of exciting machine learning model that leads to applications that bring to mind their ability to produce synthetic but realistic looking data. Generative Adversarial Networks are composed of two neural networks that work in opposite directions. In this paper, it is aimed to examine the same initial situation, same dataset, same number of iterations, parts of the same size in order to compare Generative Adversarial Networks. This paper Generative Adversarial Network (GAN), Deconvolusional Generative Adversarial Network (DCGAN), Semi-Supervised Generative Adversarial Network (SGAN/SeGAN) Conditional Generative Adversarial Network (CoGAN / CGAN) were used. These methods were calculated on the performance of MNIST dataset. The results are presented both numerically and visually.
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