  Deep neural networks (DNNs) achieve excellent performance on standard
classification tasks. However, under image quality distortions such as blur and
noise, classification accuracy becomes poor. In this work, we compare the
performance 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 DNNs
and human subjects. This could be an indication that the internal
representation of images are different between DNNs and the human visual
system. These comparisons with human performance could be used to guide future
development of more robust DNNs.
