Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: This work proposes to add a class label to both the generator and discriminator of the GAN network. This is intuitive, but is NOT novel. Conditioning the posterior distribution on the class label is an old idea. I also agree with the other reviewer that filling the appendix with a lot of new and relevant content is poor form.

The presentation is a bit sloppy. The curves in Figure 4 are missing a legend.
review_title: Not much different

final_score: rating: 2: Strong rejection
review_content: Let me preface my review by saying that I didn’t read the appendix because I think it is bad form to add a paper worth of additional material to what is supposed to be an extended abstract, and the main text unfortunately did not inspire me to read further either.

The authors propose to combine two ideas for improving generative modeling with GANs: conditioning the generator on class labels and training the discriminator to reconstruct the labels.

Given that both ideas are simple and have been used in isolation, the project has little to offer conceptually. This could still be an interesting abstract if it evaluated well the effect of combing both ideas. Unfortunately, this does not seem to be the case. 

Any evaluation based on samples is necessarily very limited, as a model which simply stores the training data will score as well as the true distribution of natural images. A more useful comparison would have been to compare samples of two generators with the same architecture and trained on the same data, one trained with the proposed changes and one without.

The value of the analysis in Figure 2 is not at all clear to me. Showing the effect of throwing away high-spatial frequency information tells me that the classifier is using that information, and that the generator is not merely interpolating low-resolution images. But it tells me very little about the effectiveness of the proposed changes to GAN training.

The paper also seems sloppily written. E.g., in the introduction the authors claim that Balle et al. (2015) describe an advance in the state of the art in image denoising. Looking at the paper I couldn’t find such a claim or a comparison to the state of the art (non-parametric methods such as BM3D and discriminative methods such as feed-forwardly trained neural nets). The authors cite Toderici et al. (2016) as an example of image models advancing compression, but to my knowledge this paper uses binarized hidden states of a recurrent neural network and no image model.
review_title: Uninsightful and in need of much more work

Decision: Reject