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Dualing GANs
[article]
2017
arXiv
pre-print
Generative adversarial nets (GANs) are a promising technique for modeling a distribution from samples. It is however well known that GAN training suffers from instability due to the nature of its maximin formulation. In this paper, we explore ways to tackle the instability problem by dualizing the discriminator. We start from linear discriminators in which case conjugate duality provides a mechanism to reformulate the saddle point objective into a maximization problem, such that both the
arXiv:1706.06216v1
fatcat:o6gcnyevbnflto2pb6dzcu4uda