text: 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.
  @cite_18: Abstract Six images of human faces were quantised into isoluminant square-shaped pixels (16 grey levels) at eight different spatial levels of quantisation. The subjects had to identify the faces that were presented with different exposure durations (from 1 to 200 msec) and with one of two brightness conditions (variable brightness in Experiment 1 or isobrightness in Experiment 2). All finer quantisation levels led to better identification than the most coarse quantisation level (15 pixels per face in the horizontal dimension) at all exposure durations. The observation of an abrupt decrease in identification efficiency on moving from 18 or more pixels per face to 15 pixels per face and the approximate equality in identification efficiency within a broad range of quantisation levels above 18 pixels per face pose some problems for existing theories of face recognition. The implications of these findings for prototype-related, auto correlation and micro genetic accounts of face and pattern processing are disc...
  @cite_1: With the advent of the Internet, billions of images are now freely available online and constitute a dense sampling of the visual world. Using a variety of non-parametric methods, we explore this world with the aid of a large dataset of 79,302,017 images collected from the Internet. Motivated by psychophysical results showing the remarkable tolerance of the human visual system to degradations in image resolution, the images in the dataset are stored as 32 x 32 color images. Each image is loosely labeled with one of the 75,062 non-abstract nouns in English, as listed in the Wordnet lexical database. Hence the image database gives a comprehensive coverage of all object categories and scenes. The semantic information from Wordnet can be used in conjunction with nearest-neighbor methods to perform object classification over a range of semantic levels minimizing the effects of labeling noise. For certain classes that are particularly prevalent in the dataset, such as people, we are able to demonstrate a recognition performance comparable to class-specific Viola-Jones style detectors.
  @cite_4: 
summary: Given that DNN performance now can match or exceed human performance, we can begin to examine more difficult problems. Humans have the capacity to recognize severely distorted images. For example, Torralba al @cite_1 show that human subjects can accurately recognize low resolution images. Similarly, Bachmann @cite_18 show that human subjects can recognize low resolution faces. Chen al @cite_4 show the effect of noise on a face recognition task. Performance is impaired, but subjects are still able to perform the task with reasonable accuracy. Given that humans can recognize to some degree under quality distortions, it is interesting if DNNs can show the same ability.