NeurIPS 2020

Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN

Meta Review

This paper was well received, and the reviewers praised it for its clarity and contributions. The idea to separate shape and semantics in the reconstruction is an interesting approach, and one that has proven quite useful. This work is likely of interest to a wide range of the NeurIPS community, those interested in computer vision as well as neuroscience. The reviewers pointed out a few places that could be clearer, but these points (for the most part) could be handled in minor revisions. One point remained unanswered about the loss of information in the HVC, and was discussed by the reviewers after seeing the rebuttal. One reviewer thought that section 3.4 didn't prove that the LVC signals do not contain the same necessary information as the HVC. Instead, it shows that the HVC signals perform better with their particular DNN architecture. Since the LVC signals have gone through less processing in the brain, it is possible that they simply require a larger network to extract the relevant information. Perhaps the authors could add this caveat to their final paper.