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 manuscript addresses an interesting solution based in machine learning techniques to classify spectra legacy data of the Hubble Space Telescope in order to publish the results and "good" spectra in Internet to scientific community. The manuscript is well-written and results are robust. Experiment is sound and the manuscript seems acceptable in the current form. The work can be short, but very interesting to the Infonor and JCC community. The main problem of the paper is that it is difficult to compare results for the classifiers utilized. A confusion matrix and parameters for each classifier could clarify results.

Answer: en

Question: Update: After reading the rebuttal comments and the revised paper, I'm leaving the rating as it was before. This paper proposes an unsupervised algorithm for transferring samples from one domain to another (related) domain under the constraint that some predefined f returns same result for the input and the result. Pros: 1. The paper presents an interesting idea of comparing samples from different domains using a fixed perceptual function f. 2. The proposed method produces visually appealing results on several datasets 3. The authors demonstrate how their approach can be used for domain adaptation and obtain improved results on the SVHN->MNIST task 4. The paper is well-written and easy to read Cons: 1. The novelty of the method is relatively minor (I consider f-constancy term as the main contribution) 2. It feels like the proposed approach would break for more dissimilar domains. The method relies on a fixed f which is trained on the source domain. This f can potentially drop information important for obtaining 1) better reconstructions in the target domain 2) more tightly related x and g(f(x)). I think the authors should consider either training all the modules in the model end-to-end or incorporating target samples into the training of f. 3. A single domain adaptation experiment is definitely not enough to consider the proposed method as a universal alternative to the existing DA approaches. I would also like to point out that using super-resolved outputs as opposed to the actual model's outputs can produce a false impression of the visual quality of the transferred samples. I'd suggest moving original outputs from the appendix into the main part.

Answer: en

Question: Some of the key details in this paper are very poorly explained or not even explained at all. The model sounds interesting and there may be something good here, but it should not be published in it's current form. Specific comments: The description of the R_l,pi convolutions in Section 2.1 was unclear. Specifically, I wasn't confident that I understood what the labels pi represented. The description of the SAEN structure in section 2.2 was worded poorly. My understanding, based on Equation 1, is that the 'shift' operation is simply a summation of the representations of the member objects, and that the 'aggregate' operation simply concatenates the representations from multiple relations. In the 'shift' step, it seems more appropriate to average over the object's member's representations h_j, rather than sum over them. The compression technique presented in Section 2.3 requires that multiple objects at a level have the same representation. Why would this ever occur, given that the representations are real valued and high-dimensional? The text is unintelligible: "two objects are equivalent if they are made by same sets of parts for all the pi-parameterizations of the R_l,pi decomposition relation." The 'ego graph patterns' in Figure 1 and 'Ego Graph Neural Network' used in the experiments are never explained in the text, and no references are given. Because of this, I cannot comment on the quality of the experiments.

Answer:
en