Generalisation in humans and deep neural networks
We compare the robustness of humans and current convolutional deep neural networks (DNNs) on object recognition under twelve different types of image degradations. First, using three well known DNNs  GoogLeNet)   we find the human visual system to be more robust to nearly all of the tested image manipulations, and we observe progressively diverging classification error-patterns between humans and DNNs when the signal gets weaker. Secondly, we show that DNNs trained directly on distorted images consistently surpass human performance on the exact distortion types they were trained on, yet they display extremely poor generalisation abilities when tested on other distortion types. For example, training on salt-and-pepper noise does not imply robustness on uniform white noise and vice versa. Thus, changes in the noise distribution between training and testing constitutes a crucial challenge to deep learning vision systems that can be systematically addressed in a lifelong machine learning approach. Our new dataset consisting of 83K carefully measured human psychophysical trials provide a useful reference for lifelong robustness against image degradations set by the human visual system.
Update after rebuttal:  This is a very interesting and well executed paper. A large number of studies have said that DNNs yield performance that resembles that of humans and animals, but this study provides a more fine-grained comparison of the behavior and finds real differences. In particular, the study probes sensitivity to perturbed inputs, something that current DNNs are much less capable at than humans. This provides a valuable dataset and benchmark to aim for, and I expect the ML community will have algorithms that perform far better in short order.   I would encourage the authors to make as fine-grained and specific comparisons of similarities and differences as possible; to suggest potential reasons why these discrepancies might exist (clearly marking these reasons as somewhat speculative if they aren't backed by direct evidence); and to expand on potential routes toward better algorithms (again marking this as speculative if it is).  _________  Summary:  This paper probes the generalization abilities of deep networks and human observers, by undertaking an extensive psychophysical investigation of human performance on ImageNet in the presence of strong distortions of the input images. The results point to a large gap between DNNs (which can perform well when trained on a specific distortion) and human observers (who can perform well across all distortions). The paper releases the psychophysical dataset and image distortions to aid future research.  Major comments:  This paper undertakes an impressively controlled comparison of human performance and DNNs at a behavioral level. While previous work has shown broad similarities between DNNs and human behavior, this evaluation undertakes a much more challenging test of generalization abilities by considering extreme distortions. Additionally, in contrast to prior work which relied on Amazon Mechanical Turk to generate data, the present submission uses carefully controlled laboratory viewing conditions that remove some prior concerns (such as variation in timing, viewing distance, and how attentive AMT subjects might be). Overall this paper contributes a rigorous psychophysical dataset that could spur a variety of follow up work in the field.  The most striking deviation between human subjects and the DNNs investigated here is in the entropy of the response distribution. The DNNs apparently become stuck, responding to all distorted images with a single class. On the one hand this is indicative of a clear failure of the DNNs in their vanilla form. On the other hand, one could imagine simple procedures to improve the situation. For instance, one could adjust the temperature parameter of the final softmax to attain equal entropy to human subjects, and assume responses are sampled from this. It is well known that the final softmax layers are not calibrated probabilities, so this may be a fairer comparison. Another route would be through explicitly calibrating the probability distribution in the final layer, eg through dropout [75].