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Semi-Supervised Learning With GANs: Revisiting Manifold Regularization
Bruno Lecouat, Chuan Sheng Foo, Houssam Zenati, Vijay Ramaseshan Chandrasekhar
Feb 12, 2018 (modified: Feb 12, 2018)ICLR 2018 Workshop Submissionreaders: everyone
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.