  Generative Adversarial Networks are powerful generative models that are able
to model the manifold of natural images. We leverage this property to perform
manifold regularization by approximating a variant of the Laplacian norm using
a Monte Carlo approximation that is easily computed with the GAN. When
incorporated into the semi-supervised feature-matching GAN we achieve
state-of-the-art results for GAN-based semi-supervised learning on CIFAR-10 and
SVHN benchmarks, with a method that is significantly easier to implement than
competing methods. We also find that manifold regularization improves the
quality of generated images, and is affected by the quality of the GAN used to
approximate the regularizer.
