A Three-Player GAN: Generating Hard Samples to Improve Classification Networks
A Three-Player GAN: Generating Hard Samples to Improve Classification Networks
We propose a Three-Player Generative Adversarial Network to improve classification networks. In addition to the game played between the discriminator and generator, a competition is introduced between the generator and the classifier. The generator's objective is to synthesize samples that are both realistic and hard to label for the classifier. …