  With great progress in the development of Generative Adversarial Networks
(GANs), in recent years, the quest for insights in understanding and
manipulating the latent space of GAN has gained more and more attention due to
its wide range of applications. While most of the researches on this task have
focused on unsupervised learning method, which induces difficulties in training
and limitation in results, our work approaches another direction, encoding
human's prior knowledge to discover more about the hidden space of GAN. With
this supervised manner, we produce promising results, demonstrated by accurate
manipulation of generated images. Even though our model is more suitable for
task-specific problems, we hope that its ease in implementation, preciseness,
robustness, and the allowance of richer set of properties (compared to other
approaches) for image manipulation can enhance the result of many current
applications.
