Title: Comparing deep neural networks against humans: object recognition when the signal gets weaker,
Abstract: Human visual object recognition is typically rapid and seemingly effortless,
as well as largely independent of viewpoint and object orientation. Until very
recently, animate visual systems were the only ones capable of this remarkable
computational feat. This has changed with the rise of a class of computer
vision algorithms called deep neural networks (DNNs) that achieve human-level
classification performance on object recognition tasks. Furthermore, a growing
number of studies report similarities in the way DNNs and the human visual
system process objects, suggesting that current DNNs may be good models of
human visual object recognition. Yet there clearly exist important
architectural and processing differences between state-of-the-art DNNs and the
primate visual system. The potential behavioural consequences of these
differences are not well understood. We aim to address this issue by comparing
human and DNN generalisation abilities towards image degradations. We find the
human visual system to be more robust to image manipulations like contrast
reduction, additive noise or novel eidolon-distortions. In addition, we find
progressively diverging classification error-patterns between humans and DNNs
when the signal gets weaker, indicating that there may still be marked
differences in the way humans and current DNNs perform visual object
recognition. We envision that our findings as well as our carefully measured
and freely available behavioural datasets provide a new useful benchmark for
the computer vision community to improve the robustness of DNNs and a
motivation for neuroscientists to search for mechanisms in the brain that could
facilitate this robustness.