  Some recent artificial neural networks (ANNs) claim to model aspects of
primate neural and human performance data. Their success in object recognition
is, however, dependent on exploiting low-level features for solving visual
tasks in a way that humans do not. As a result, out-of-distribution or
adversarial input is often challenging for ANNs. Humans instead learn abstract
patterns and are mostly unaffected by many extreme image distortions. We
introduce a set of novel image transforms inspired by neurophysiological
findings and evaluate humans and ANNs on an object recognition task. We show
that machines perform better than humans for certain transforms and struggle to
perform at par with humans on others that are easy for humans. We quantify the
differences in accuracy for humans and machines and find a ranking of
difficulty for our transforms for human data. We also suggest how certain
characteristics of human visual processing can be adapted to improve the
performance of ANNs for our difficult-for-machines transforms.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning