Versatile Auxiliary Classifier + Generative Adversarial Network (VAC+GAN); Training Conditional Generators
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One of the most interesting challenges in Artificial Intelligence is to train conditional generators which are able to provide labeled fake samples drawn from a specific distribution. In this work, a new framework is presented to train a deep conditional generator by placing a classifier in parallel with the discriminator and back propagate the classification error through the generator network. The method is versatile and is applicable to any variations of Generative Adversarial Network (GAN) implementation, and also is giving superior results compare to similar methods.
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