Deep neural networks (DNNs) are machine learning algorithms that have revolutionized
computer vision due to their remarkable successes in tasks like object classification and …

We compare the robustness of humans and current convolutional deep neural networks
(DNNs) on object recognition under twelve different types of image degradations. First, using …

An ideal observer is a theoretical model observer that performs a specific sensory-
perceptual task optimally, making the best possible use of the available information given …

Human vision is heterogeneous around the visual field. At a fixed eccentricity, performance
is better along the horizontal than the vertical meridian and along the lower than the upper …

We investigate how the population nonlinearities resulting from lateral inhibition and
thresholding in sparse coding networks influence neural response selectivity and …

Divisive normalization (DN) is a prominent computational building block in the brain that has
been proposed as a canonical cortical operation. Numerous experimental studies have …

Visual performance varies around the visual field. It is best near the fovea compared to the
periphery, and at iso-eccentric locations it is best on the horizontal, intermediate on the …

A serious obstacle for understanding early spatial vision comes from the failure of the so-
called standard model (SM) to predict the perception of plaid masking. But the SM originated …

In vision science, cascades of Linear+ Nonlinear transforms are very successful in modeling
a number of perceptual experiences. However, the conventional literature is usually too …

One well-established characteristic of early visual processing is the contrast sensitivity
function (CSF) which describes how sensitivity varies with the spatial frequency (SF) content …