Reviews: Generative Well-intentioned Networks

The method, using GANs to map low confidence examples to high confidence examples of the same class is highly original and shows promise as an effective method. The authors also provide a good description of how their work differs from Defense-GAN, and also notes some of the prior work on classifiers with a reject option (although they should also cite some of the more recent work doing so with DNNs e.g. The paper is clearly written, however there seems to be some information gaps with regards to network architecture and the mechanism for conditioning on the low confidence image x. Additionally there are several methods existing in the literature for conditioning the discriminator on label information: [1], [2], and concatenation and it's not clear from the paper which is used. The main drawback of this paper is that the datasets used (MNIST and FashionMNIST) are too toy to allow the reader to draw informed conclusions.