  A key challenge in understanding the sensory transformations of the visual
system is to obtain a highly predictive model of responses from visual cortical
neurons. Deep neural networks (DNNs) provide a promising candidate for such a
model. However, DNNs require orders of magnitude more training data than
neuroscientists can collect from real neurons because experimental recording
time is severely limited. This motivates us to find images that train
highly-predictive DNNs with as little training data as possible. We propose
gaudy images---high-contrast binarized versions of natural images---to
efficiently train DNNs. In extensive simulation experiments, we find that
training DNNs with gaudy images substantially reduces the number of training
images needed to accurately predict the simulated responses of visual cortical
neurons. We also find that gaudy images, chosen before training, outperform
images chosen during training by active learning algorithms. Thus, gaudy images
overemphasize features of natural images, especially edges, that are the most
important for efficiently training DNNs. We believe gaudy images will aid in
the modeling of visual cortical neurons, potentially opening new scientific
questions about visual processing, as well as aid general practitioners that
seek ways to improve the training of DNNs.
