A fundamental, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether GANs are actually able to capture the key characteristics of the datasets they are trained on. The current approaches to examining this issue require significant human supervision, such as visual inspection of sampled images, and often offer only fairly limited scalability. In this paper, we propose new techniques that employ classification-based perspective to evaluate synthetic GAN distributions and their capability to accurately reflect the essential properties of the training data. These techniques require only minimal human supervision and can easily be scaled and adapted to evaluate a variety of state-of-the-art GANs on large, popular datasets. They also indicate that GANs have significant problems in reproducing the more distributional properties of the training dataset. In particular, the diversity of such synthetic data is orders of magnitude smaller than that of the original data.
Overall comments: Trying to shed light at comparison between different GAN variants, but the metrics introduced are not very novel, results are not comparable with prior work and older version of certain models are used (WGAN instead of Improved WGAN)
Section 2.1: quantifying mode collapse
* This section should mention Inception scores. A model which collapses on only one class will have a low inception score, and this metric also uses a conv net classifier, as the approach is very similar (the method is only mentioned briefly in section 2.3)
* The authors might not be aware of concurrent work published before the ICLR deadline, which introduces a very similar metric: https://arxiv.org/abs/1706.08500
Section 2.2: measuring diversity:
* There is an inherent flaw in this metric, namely it trains one GAN per class. One cannot generalize from this metric on how different GAN models will perform when trained on the entire dataset. One model might be able to capture more diverse distributions, but lose a bit of quality, while another model might be able to create good samples when train on low diversity data. We already know that when looking at other generative models, we can find such examples. VAEs can obtain very good samples on celebA, a dataset with relative low diversity, but not so good samples on cifar. 
* The authors compare their experiment with Radford et al. (2015), but that needs to be done with caution. In Radford et al. (2015), the authors use a conditional generative model trained on the entire dataset. In that setting, this test is more suitable since one can test how good well the model has learned the conditioning. For example, for a conditional model trained on cats and dogs, a failure mode is that the model generates only cats. This failure mode can then be captured by this metric. However, when training two models, one on cats and one on dogs, this failure mode is not present since the data is already split into classes. 
* The proposed metric is not necessarily a diversity metric, it is also a quality metric:  in a situation where all the models diverge and generate random noise, with high diversity, but without any structure. This metric would capture this issue, because a classifier will not be able to learn the classes, because there is no correlation between the classes and the generated images. 
Experimental results:
* Positive insights regarding labels and celeba. Looks like subtle labels on faces are not being captured by GAN models. 
Figure 1 is hard to read. 
* ALI having higher diversity on celeba is explicitly mentioned in a paper the authors cite, namely “Do GANs actually learn the distribution? An empirical study”. Would be nice to mention that in the paper.
Would like to see:
* A comparison with the Improved Wasserstein GAN model. This model is now the one used by the community, as opposed to the original Wasserstein GAN.
* Models trained on cifar, with the reported inception scores of the models on cifar. That makes the paper comparable with previous work and is a test against bugs in model implementations or other parts of the code. This would also allow to test for claims such as the fact that the Improved GAN has more mode collapse than DCGAN, while the Improved GAN paper says the opposite. 
The reason why the authors chose the models they did for comparison. In the BiGAN (same model as ALI) paper the authors report a low inception score, which suggests that their model is not able to capture the subtleties of the Cifar dataset, and this seems to be correlated with the results obtained in this work.