Manifold regularization with GANs for semi-supervised learning

Understand

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.