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].

Input: Consider Input: 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.

Output: en


Input: Consider Input: This paper essentially presents a new inductive bias in the architecture of (convolutional) neural networks (CNN). The mathematical motivations/derivations of the proposed architecture are detailed and rigorous. The proposed architecture promises to produce equivariant representations with steerable features using fewer parameters than traditional CNNs, which is particularly useful in small data regimes. Interesting and novel connections are presented between steerable filters and so called “steerable fibers”. The architecture is strongly inspired by the author’s previous work, as well as that of “capsules” (Hinton, 2011). The proposed architecture is compared on CIFAR10 against state-of-the-art inspired architectures (ResNets), and is shown to be superior particularly in the small data regime. The lack of empirical comparison on large scale dataset, such as ImageNet or COCO makes this largely a theoretical contribution. I would have also liked to see more empirical evaluation of the equivariance properties. It is not intuitively clear exactly why this architecture performs better on CIFAR10 as it is not clear that capturing equivariances helps to classify different instances of object categories. Wouldn’t action-recognition in videos, for example, not be a better illustrative dataset?

Output: en


Input: Consider Input: El artículo es especialmente válido como una guía de implementación de un data center. Su nivel teórico no es relevante, sin embargo su aplicabilidad le hace de interés.  El artículo está bien escrito y documentado.  Se debe insistir que se trata de un caso y este caso está desarrollado completo siguiendo las normas TIA propuestas como referencia.


Output: es