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

Versatile Auxiliary Classifier with Generative Adversarial Network (VAC+GAN), Multi Class Scenarios