Q: 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].
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?
A:
en