Task: Language Identification
Input (Spanish, English): Building on earlier work on a model called NICE, this paper presents an approach to constructing deep feed-forward generative models. The model is evaluated on several datasets. While it does not achieve state-of-the-art performance, it advances an interesting class of models. The paper is mostly well written and clear.  Given that inference and generation are both efficient and exact, and given that this represents a main advantage over other models, it would be great if the authors could provide some motivating example applications where this is needed/would be useful.  The authors claim that “unlike both variational autoencoders and GANs, our technique is able to learn a semantically meaningful latent space which is as high dimensional as the input space.” Where is the evidence for this claim? I didn’t see any analysis of the semantic meaningfulness of the latent space learned by real NVP. Stronger evidence that the learned representations are actually useful for downstream tasks would be nice.  I still think the author’s intuitions around the “fixed reconstruction cost of L2” are very vague. The factorial Gaussian assumption itself does not limit the generative model, it merely smoothes an otherwise arbitrary distribution, and to a degree which can be arbitrarily small, p(x) = \int p(z) N(x | f(z), \sigma^2) dz. How a lose lower bound plays into this is not clear from the paper.
Output (English): en