Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Semi-Supervised Learning With GANs: Revisiting Manifold Regularization
Bruno Lecouat, Chuan Sheng Foo, Houssam Zenati, Vijay Ramaseshan Chandrasekhar
Feb 12, 2018 (modified: Jun 04, 2018)ICLR 2018 Workshop Submissionreaders: everyoneShow Bibtex
Abstract: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 Salimans et al. (2016), we achieve state-of-the-art results for GAN-based semisupervised
learning on the CIFAR-10 dataset, with a method that is significantly
easier to implement than competing methods.