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Deep neural networks (DNNs) achieve excellent performance on standardclassification tasks. However, under image quality distortions such as blur andnoise, classification accuracy becomes poor. In this work, we compare theperformance of DNNs with human subjects on distorted images. We show that,although DNNs perform better than or on par with humans on good quality images,DNN performance is still much lower than human performance on distorted images.We additionally find that there is little correlation in errors between DNNsand human subjects. This could be an indication that the internalrepresentation of images are different between DNNs and the human visualsystem. These comparisons with human performance could be used to guide futuredevelopment of more robust DNNs.

A Study and Comparison of Human and Deep Learning Recognition Performance Under Visual Distortions