text: This paper presents a semi-supervised extension for applying GANs to regression tasks. The authors propose two architectures: one adds a supervised regression loss to the standard unsupervised GAN discriminator loss. The other replaces the real/fake output of the discriminator with only a real-valued output and then applies a kernel on top of this output to predict if samples are real or fake. The methods are evaluated on a public driving dataset, and are shown to outperform an Improved-GAN which predicts the real-valued labels discretized into 10 classes. This is a nice idea, but I am not completely convinced by the experimental results. The proposed method is compared to Improved-GAN where the real-valued labels are discretized into 10 classes. Why 10? How was this chosen? The authors rightfully state that "[...] this discretization will add some unavoidable quantization error to our training" (Sec 5.2) and then again in the conclusion "determining the number of [discretization] classes for each application is non-trivial", yet nowhere do they explore the effects of this. Surely, this is a very important part of the evaluation? And surely as we improve the discretization-resolution the gap between the two will close? This needs to be evaluated. Also, the main motivation for a GAN-based regression model is based on the paucity of labeled training data. However, this is another place where the argument would greatly benefit from some empirical backing. I.e., I would really at least like to see how a discriminative regression model (e.g. a pretrained convnet fine-tuned for regression) compares to the proposed technique when trained (fine-tuned) only on the (smaller) labeled data set, perhaps augmented with standard image augmentation techniques to increase the size. Overall, I found the paper a little hard to read (especially understanding how Architecture 2 works and moreover what its motivation is) and empirical evaluation a bit lacking. I also found the claims of "solving" the regression task using GANs unfounded based on the experimental results presented. In conclusion, while the technique looks promising, the novelty seems fairly low and the evaluation can benefit from one or more additional baselines (at the very least showing how varying the discretization resolution of the Improved-GAN affects the results, but preferably one or more discriminative baselines), and also perhaps on one or more additional data sets to showcase the technique's generality. Nits:Several part are quite repetitive and can benefit from a rewrite. Particularly the last paragraphs in the Introduction. Section 3:  notation seems inconsistent (p_z(z) vs P_z(z) directly below in Eqn 1) The second architecture needs to be explained a little better, and motivated a little better. Eqn 5: I think it should be 0 \geq \hat{y}, and not 0 \leq \hat{y}