In this paper, to handle this problem, we propose to use adversarial training (Goodfellow et al., 2014;Denton et al., 2015;Li et al., 2017), which has achieved success in image generation (Radford et al., 2015;Gulrajani et al., 2017;Berthelot et al., 2017).
training GAN is a non-trivial task and there are some previous researches that investigate methods to improve training performance, such as Wasserstein GAN (WGAN)  and Energybased GAN (EGAN) (Salimans et al., 2016;Gulrajani et al., 2017;Zhao et al., 2017;Berthelot et al., 2017).