  Conditional generators learn the data distribution for each class in a
multi-class scenario and generate samples for a specific class given the right
input from the latent space. In this work, a method known as "Versatile
Auxiliary Classifier with Generative Adversarial Network" for multi-class
scenarios is presented. In this technique, the Generative Adversarial Networks
(GAN)'s generator is turned into a conditional generator by placing a
multi-class classifier in parallel with the discriminator network and
backpropagate the classification error through the generator. This technique is
versatile enough to be applied to any GAN implementation. The results on two
databases and comparisons with other method are provided as well.
