  GANS are powerful generative models that are able to model the manifold of
natural images. We leverage this property to perform manifold regularization by
approximating the Laplacian norm using a Monte Carlo approximation that is
easily computed with the GAN. When incorporated into the feature-matching GAN
of Improved GAN, we achieve state-of-the-art results for GAN-based
semi-supervised learning on the CIFAR-10 dataset, with a method that is
significantly easier to implement than competing methods.
