  Recently introduced generative adversarial network (GAN) has been shown
numerous promising results to generate realistic samples. The essential task of
GAN is to control the features of samples generated from a random distribution.
While the current GAN structures, such as conditional GAN, successfully
generate samples with desired major features, they often fail to produce
detailed features that bring specific differences among samples. To overcome
this limitation, here we propose a controllable GAN (ControlGAN) structure. By
separating a feature classifier from a discriminator, the generator of
ControlGAN is designed to learn generating synthetic samples with the specific
detailed features. Evaluated with multiple image datasets, ControlGAN shows a
power to generate improved samples with well-controlled features. Furthermore,
we demonstrate that ControlGAN can generate intermediate features and opposite
features for interpolated and extrapolated input labels that are not used in
the training process. It implies that ControlGAN can significantly contribute
to the variety of generated samples.
