Cooperate or Compete: A New Perspective on Training of Generative Networks

Sobhan Babu

Keywords: Generative Adversarial Networks, Nash Equilibrium, Correlated Equilibrium, Repeated Games

TL;DR: Generative networks can perform better and learn faster if training is modeled as an infinitely repeated simultaneous game

Abstract: GANs have two competing modules: the generator module is trained to generate new examples, and the discriminator module is trained to discriminate real examples from generated examples. The training procedure of GAN is modeled as a finitely repeated simultaneous game. Each module tries to increase its performance at every repetition of the base game (at every batch of training data) in a non-cooperative manner. We observed that each module can perform better and learn faster if training is modeled as an infinitely repeated simultaneous game. At every repetition of the base game (at every batch of training data) the stronger module (whose performance is increased or remains the same compared to the previous batch of training data) cooperates with the weaker module (whose performance is decreased compared to the previous batch of training data) and only the weaker module is allowed to increase its performance.

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