Generalization and Equilibrium in Generative Adversarial Nets (GANs)

Generative Adversarial Networks (GANs) [Goodfellow et al., 2014] have become one of the dominant methods for fitting generative models to complicated real-life data, and even found unusual uses such as designing good cryptographic primitives [Abadi and Andersen, 2016]. See a survey by Goodfellow [2016]. Various novel architectures and training objectives were introduced to address perceived shortcomings of the original idea, leading to more stable training and more realistic generative models in practice (see Odena et al. [2016], Huang et al. [2017], Radford et al. [2016], Tolstikhin et al. [2017], Salimans et al. [2016], Jiwoong Im et al. [2016], Durugkar et al. [2016] and the reference therein).