In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].
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Question: The reviewers seem to agree that the framework presented is not very novel, something I agree with.  The experiments show that the low rank + diagonal parameterization can be useful, however. The paper could be improved by making a more tightened message, and clearer arguments. As it currently stands, however it does not seem ready for publication in ICLR.

Answer: en


Question: In this interesting paper the authors explore the idea of using an ensemble of multiple discriminators in generative adversarial network training. This comes with a number of benefits, mainly being able to use less powerful discriminators which may provide better training signal to the generator early on in training when strong discriminators might overpower the generator.  My main comment is about the way the paper is presented. The caption of Figure 1. and Section 3.1 suggests using the best discriminator by taking the maximum over the performance of individual ensemble members. This does not appear to be the best thing to do because we are just bound to get a training signal that is stricter than any of the individual members of the ensemble. Then the rest of the paper explores relaxing the maximum and considers various averaging techniques to obtain a ’soft-discriminator’. To me, this idea is far more appealing, and the results seem to support this, too. Skimming the paper it seems as if the authors mainly advocated always using the strongest discriminator, evidenced by my premature pre-review question earlier.  Overall, I think this paper is a valuable contribution, and I think the idea of multiple discriminators is an interesting direction to pursue.

Answer: en


Question: This paper makes a valuable contribution to provide a more clear understanding of generative adversarial network (GAN) training procedure.   With the new insight of the training dynamics of GAN, as well as its variant, the authors reveal the reason that why the gradient is either vanishing in original GAN or unstable in its variant. More importantly, they also provide a way to avoid such difficulties by introducing perturbation. I believe this paper will inspire more principled research in this direction.   I am very interested in the perturbation trick to avoid the gradient instability and vanishment. In fact, this is quite related to dropout trick in where the perturbation can be viewed as Bernoulli distribution. It will be great if the connection can be discussed.  Besides the theoretical analysis, is there any empirical study to justify this trick? Could you please add some experiments like Fig 2 and 3 for the perturbated GAN for comparison?

Answer:
en