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We propose a Three-Player Generative Adversarial Network to improveclassification networks. In addition to the game played between thediscriminator and generator, a competition is introduced between the generatorand the classifier. The generator's objective is to synthesize samples that areboth realistic and hard to label for the classifier. Even though we make noassumptions on the type of augmentations to learn, we find that the model isable to synthesize realistically looking examples that are hard for theclassification model. Furthermore, the classifier becomes more robust whentrained on these difficult samples. The method is evaluated on a public datasetfor traffic sign recognition.

A Three-Player GAN: Generating Hard Samples To Improve Classification Networks