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: This paper makes three main methodological contributions:  - definition of Neural Feature (NF) as the pixel average of the top N images that highly activation a neuron  - ranking of neurons based on color selectivity  - ranking of neurons based on class selectivity  The main weaknesses of the paper are that none of the methodological contributions are very significant, and no singularly significant result arises from the application of the methods.  However, the main strengths of the paper are its assortment of moderately-sized interesting conclusions about the basic behavior of neural nets. For example, a few are:  - “Indexing on class selectivity neurons we found highly class selective neurons like digital-clock at conv2, cardoon at conv3 and ladybug at conv5, much before the fully connected layers.” As far as I know, this had not been previously reported.  - Color selective neurons are found even in higher layers. (25% color selectivity in conv5)  - “our main color axis emerge (black-white, blue-yellow, orange-cyan and cyan- magenta). Curiously, these two observations correlate with evidences in the human visual system (Shapley & Hawken (2011)).” Great observation!  Overall, I’d recommend the paper be accepted, because although it’s difficult to predict at this time, there’s a fair chance that one of the “smaller conclusions” would turn out to be important in hindsight a few years hence.   Other small comments:  - The cite for “Learning to generate chairs…” is wrong (first two authors combined resulting in a confusing cite)   - What exactly is the Color Selectivity Index computing? The Opponent Color Space isn’t well defined and it wasn’t previously familiar to me. Intuitively it seems to be selecting for units that respond to a constant color, but the highest color selectivity NF in Fig 5 i for a unit with two colors, not one. Finally, the very last unit (lowest color selectivity) is almost the same edge pattern, but with white -> black instead of blue -> orange. Why are these considered to be so drastically different? This should probably be more clearly described.   - For the sake of argument, imagine a mushroom sensitive neuron in conv5 that fires highly for mushrooms of *any* color but not for anything else. If the dataset contains only red-capped mushrooms, would the color selectivity index for this neuron be high or low? If it is high, it’s somewhat misleading because the unit itself actually isn’t color selective; the dataset just happens only to have red mushrooms in it. (It’s a subtle point but worth considering and probably discussing in the paper)

Answer: en


Question: This paper is motivated by the ability that human's visual system can recognize contents of environment by from critical features, and tried to investigate whether neural networks can also have this kind of ability.  Specifically, the paper proposed to use Auto-Encoder (AE) as the network to reconstruct the low fidelity of visual input. Moreover, similar to Mnih et al. (2014),  the paper also proposed to use a recurrent fashion to mimic the sequential behavior the  human visual system.   I think the paper is well motivated. However, there are several concerns: 1. The baselines of the paper are too weak. Nearest neighbor, bilinear, bicubic and cubic interpolations without any learning procedure are of course performed worse than AE based models. The author should compare with the STOA methods such as

Answer: en


Question:  It has recently come to my attention that the objective proposed by the authors in this paper has in fact already been studied in the literature under the name 'reward-weighted regression', from e.g. ICML 2007 [1]. This has spawned several other works using the same objective (e.g. [2]). One can examine for instance the objective proposed in Section 3.4 from [2], from ICANN 2008.  While this paper has already been accepted to ICLR, it would be beneficial for the authors to at least cite these works (and other related works) so that readers are aware of the previous origins of this idea.  [1] 

Answer:
en