Reviews: Good Semi-supervised Learning That Requires a Bad GAN

After reading the rebuttal I changed my score to 7. Overall it is an interesting paper with an interesting idea. Although the theoretical contributions are emphasized I find the empirical findings more appealing. The theory presented in the paper is not convincing (input versus feature, convexity etc). I think the link to classical semi-supervised learning and the cluster assumption should be emphasized, and the * low density assumption on the boundary* as explained in this paper: Semi-Supervised Classification by Low Density Separation Olivier Chapelle, Alexander Zien http://citeseerx.ist.psu.edu/viewdoc/download?doi 10.1.1.76.5826&rep rep1&type pdf I am changing my review to 7, and I hope that the authors will put their contribution in the context of known work in semi-supervised learning, that the boundary of separation should lie in the low density regions . This will put the paper better in context.