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].

[Q]: La ponencia es muy completa es un tema importante y original, con una calidad tecnica muy adecuada y una presentacion magnifica
[A]: es

[Q]: The paper describes a network architecture for inverse problems in computer vision. Example inverse problems considered are image inpainting, computing intrinsic image decomposition and foreground/background separation. The architecture is composed of (i) a generator that produces target (latent) output (such as foreground / background regions), (ii) renderer that composes that latent output back to the image that can be compared with the input to measure reconstruction error, and (iii) adversarial prior that ensures the target output (latent) image respects a certain image statistics. Strong points. - The proposed architecture with memory database is interesting and appears to be novel. Weak points: - Experimental results are only proof-of-concept in toy set-ups and do not clearly demonstrate benefits of the proposed architecture. - It is unclear whether the memory retrieval engine that retrieves images based on L2 distance on pixel values is going generalize to other more realistic scenarios. - Clarity. The clarity of explanation can be also improved (see below). Detailed evaluation. Originality: - The novelty of this work lies in the (iii) adversarial prior that places an adversarial loss between the generated latent output and a single image retrieved from a large unlabelled database of target output examples (called memory). The adversarial prior has a convolutional form matching local image statistics, rather than the entire image. The particular form of network architecture with the memory-based fully convolutional adversarial loss appears to be novel and potentially interesting. - Motivation for the Architecture. The weakest point of the proposed architecture is the "Memory retrieval engine" R (section 2.4), where images are retrieved from the memory by measuring L2 distance on pixel intensities. While this maybe ok for simple problems considered in this work, it is unclear how this can generalize to other more complicated datasets and problems. This should be better discussed, better justified and ideally results in some more realistic set-up shown (see below). Quality: - Experiments. Results are shown for inpainting of MNIST digits, intrinsic image decomposition on the MIT intrinsic image database, and figure/ground layer extraction on the synthesized dataset of 3D chairs rendered onto background from real photographs. The experimental validation of the model is not very strong and proof-of-concept only. All the experiments are performed in simplified toy set-ups. The MNIST digit inpainting is far from current state-of-the-art on image inpainting in real photographs (see e.g. Pathak et al., 2016). The foreground background separation is done on only synthetically generated test data. Even for intrinsic image demposition problem there is now relatively large-scale dataset of (Bell et al., 2014), see the citation below. While this is probably ok for the ICLR paper, it diminishes the significance of the work. Is this model going to be useful in a real settings? One possibility to address this would be to focus on one of the problems and show results on a challenging state-of-the-art data. It would be great to see the benefits of the memory database. S. Bell, K. Bala, and N. Snavely. Intrinsic images in the wild. ACM Transactions on Graphics, 33(4):159, 2014. Clarity: - The clarity of the writing can be improved. I found some of the terminology of the paper, specially the "imagination" and "memory" confusing. From figure 2, it is not clear how the "memories" for the given input image are obtained, which also took me some time to understand. - To help understand the proposed architecture, it would be useful to draw an illustration of what is happening in the "feature space", similar in spirit e.g. to figure 2 in
[A]: en

[Q]: The paper reports several connections between the image representations in state-of-the are object recognition networks and findings from human visual psychophysics: 1) It shows that the mean L1 distance in the feature space of certain CNN layers is predictive of human noise-detection thresholds in natural images. 2) It reports that for 3 different 2-AFC tasks for which there exists a condition that is hard and one that is easy for humans, the mutual information between decision label and quantised CNN activations is usually higher in the condition that is easier for humans. 3) It reproduces the general bandpass nature of contrast/frequency detection sensitivity in humans. While these findings appear interesting, they are also rather anecdotal and some of them seem to be rather trivial (e.g. findings in 2). To make a convincing statement it would be important to explore what aspects of the CNN lead to the reported findings. One possible way of doing that could be to include good baseline models to compare against. As I mentioned before, one such baseline should be reasonable low-level vision model. Another interesting direction would be to compare the results for the same network at different training stages. In that way one might be able to find out which parts of the reported results can be reproduced by simple low-level image processing systems, which parts are due to the general deep network's architecture and which parts arise from the powerful computational properties (object recognition performance) of the CNNs. In conclusion, I believe that establishing correspondences between state-of-the art CNNs and human vision is a potentially fruitful approach. However to make a convincing point that found correspondences are non-trivial, it is crucial to show that non-trivial aspects of the CNN lead to the reported findings, which was not done. Therefore, the contribution of the paper is limited since I cannot judge whether the findings really tell me something about a unique relation between high-performing CNNs and the human visual system. UPDATE: Thank you very much for your extensive revision and inclusion of several of the suggested baselines. The results of the baseline models often raise more questions and make the interpretation of the results more complex, but I feel that this reflects the complexity of the topic and makes the work rather more worthwhile. One further suggestion: As the experiments with the snapshots of the CaffeNet shows, the direct relationship between CNN performance and prediction accuracy of biological vision known from Yamins et al. 2014 and Cadieu et al. 2014 does not necessarily hold in your experiments. I think this should be discussed somewhere in the paper. All in all, I think that the paper now constitutes a decent contribution relating state-of-the art CNNs to human psychophysics and I would be happy for this work to be accepted. I raise the my rating for this paper to 7.
[A]:
en