Task: Language Identification
Input (Spanish, English): This paper proposes Generative Adversarial Parallelization (GAP), one schedule to train N Generative Adversarial Networks (GANs) in parallel. GAP proceeds by shuffling the assignments between the N generators and the N discriminators at play every few epochs. Therefore, GAP forces each generator to compete with multiple discriminators at random. The authors claim that such randomization reduces undesired "mode collapsing behaviour", typical of GANs.  I have three concerns with this submission.  1) After training the N GANs for a sufficient amount of time, the authors propose to choose the best generator using the GAM metric. I oppose to this because of two reasons. First, a single GAN will most likely be unable to express the full richness of the true data begin modeled. Said differently, a single generator with limited power will either describe a mode well, or describe many modes poorly. Second, GAM relies on the scores given by the discriminators, which can be ill-posed (focus on artifacts). Since there is There is nothing wrong with mode collapsing when this happens under control. Thus, I believe that a better strategy would be to not choose and combine all generators into a mixture. Of course, this would require a way to decide on mixture weights. This can be done, for instance, using rejection sampling based on discriminator scores.  2) The authors should provide a theoretical (or at least conceptual) comparison to dropout. In essence, this paper has a very similar flavour: every generator is competing against all N discriminators, but at each epoch we drop N-1 for every generator. Related to the previous point, after training dropout keeps all the neurons, effectively approximating a large ensemble of neural networks.  3) The qualitative results are not convincing. Most of the figures show only results about GAP. How do the baseline samples look like? The GAN and LAPGAN papers show very similar samples. On the other hand, I do not find Figures 3 and 4 convincing: for instance, the generator in Figure 3 was most likely under-parametrized.  As a minor comment, I would remove Figure 2. This is because of three reasons: it may be protected by copyright, it occupies a lot of space, and it does not add much value to the explanation. Also, the indices (i_t) are undefined in Algorithm 1.  Overall, this paper shows good ideas, but it needs further work in terms of conceptual development and experimental evaluation.
Output (English): en