DzGAN: Improved Conditional Generative Adversarial Nets Using Divided Z-Vector

Conditional Generative Adversarial Nets [1](cGAN) was recently proposed as a novel conditional learning method by feeding some extra information into the network. In this paper we propose an improved conditional GANs which use divided z-vector (DzGAN). The computation amount will be reduced because DzGAN can implement conditional learning using not images but one-hot vector by dividing the range of z-vector (e.g. -1\textasciitilde1 to -1\textasciitilde0 and 0\textasciitilde1). In the DzGAN, the discriminator is fed by the images with label using one-hot vector and the generator is fed by divided z-vector (e.g. there are 10 classes In MNIST dataset, the divided z-vector will be z1\textasciitildez10 accordingly) with corresponding label fed into the discriminator, thus we can implement conditional learning. In this paper we use conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) [7] instead of cGAN because cDCGAN can generate clear image better than cGAN. Heuristic experiments of conditional learning which compare the computation amount demonstrate that DzGAN is superior than cDCGAN.