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Summary: This paper draws inspiration from work on psychophysics on classification images. Large-scale human experiments were run, where people were asked to classify images generated from random noise (randomly generated by inverting HOG or CNN feature spaces to more closely approximate the distribution of natural images). The results were used to 1) visualize human perception of different classes, 2) see how well classifiers trained on datasets of random noise would work on real images, and 3) use the results as an additional source of information to regularize classifiers trained on a small number of images. Quality: This is a very unusual paper. It is overall a high quality and well written paper where interesting and novel experiments were carried out; however it is unclear if the results or methods of the paper are of practical value.