Controllable Generative Adversarial Network

Although it is recently introduced, in last few years, generative adversarial network (GAN) has been shown many promising results to generate realistic samples. However, it is hardly able to control generated samples since input variables for a generator are from a random distribution. Some attempts have been made to control generated samples from GAN, but they have shown moderate results. Furthermore, it is hardly possible to control the generator to concentrate on reality or distinctness. For example, with existing models, a generator for face image generation cannot be set to concentrate on one of the two objectives, i.e. generating realistic face and generating difference face according to input labels. Here, we propose controllable GAN (CGAN) in this paper. CGAN shows powerful performance to control generated samples; in addition, it can control the generator to concentrate on reality or distinctness. In this paper, CGAN is evaluated with CelebA datasets. We believe that CGAN can contribute to the research in generative neural network models.