Stabilizing Training Of Generative Adversarial Networks Through Regularization

This Work Proposes A New Regularization Approach With Low Computational Cost That Yields A Stable Gan Training Procedure And Demonstrates The Effectiveness Of This Regularizer.

Stabilizing training of generative adversarial networks through regularization kevin roth, aurelien lucchi, sebastian nowozin, thomas hofmann presented by dixin luo. Implementation of the nips17 paper stabilizing training of generative adversarial networks through regularization. Towards principled methods for training generative adversarial networks.

Martin Arjovsky, Soumith Chintala, And Léon Bottou.

Deep generative models based on generative adversarial networks (gans) have demonstrated impressive sample quality but in order to work they require a careful choice of architecture,.