text: The paper proposes a new evaluation measure for evaluating GANs. Specifically, the paper proposes generating synthetic images using GAN, training a classifier (for an auxiliary task, not the real vs fake discriminator) and measuring the performance of this classifier on held out real data. While the idea of using a downstream classification task to evaluate the quality of generative models has been explored before  (e.g. semi-supervised learning), I think that this is the first paper to evaluate GANs using such an evaluation metric. I'm not super convinced that this is an useful evaluation metric as the absolute number is somewhat to interpret and dependent on the details of the classifier used. The results in Table 1 change quite a bit depending on the classifier. It would be useful to add a discussion of the failure modes of the proposed metric. It seems like a generator which generates samples close to the classification boundary (but drops examples far away from the boundary) could still achieve a high score under this metric. In the experiments, were different architectures used for different GAN variants? I think the mode-collapse evaluation metrics in MR-GAN are worth discussing in Section 2.1 Mode Regularized Generative Adversarial Networks https://arxiv.org/abs/1612.02136