Task: Language Identification
Input (Spanish, English): 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.
Output (English): en