The comparison between Conditional Generative Adversarial Nets and Deep Convolutional Generative Adversarial Network, and its GUI-related application

In recent years, Generative Adversarial Nets (GAN), Conditional Generative Adversarial Nets (CGAN), and Deep convolutional generative adversarial networks (DCGAN) have generally been well-received in Artificial Intelligence (AI) industry. This paper first briefly introduces the fundamentals of GAN, CGAN, and DCGAN. Next, we focus on comparing two improved GAN variants- CGAN and DCGAN. To be specific, we train them with certain architectural constraints on two datasets - MNIST and Animation images. We show convincing evidence that DCGAN outperforms CGAN in terms of processing image datasets to a large extent. Additionally, we make a Graphical User Interface (GUI), enabling users to choose face photos with different tags generated by DCGAN.