Abstract: Novel one vs. all based extensions to Generative Adversarial Networks (GANs) are disclosed, which can be applied to multiclass classification problems with changing classes in a distributed setting. GANs can be used in semi-supervised classification by providing the class label information to discriminator from real training data. Instead of using the discriminator as a label classifier, a separate network component or module—referred to as head discriminator—is appended which labels the input instances created by the generator. The discriminator is kept as a binary classifier (as in existing GANs) which only differentiates between true data and the output of the generator. The newly added head discriminator learns to discriminate between one vs all class from the generator's output.