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 …

The many successes of deep neural networks (DNNs) over the past decade have largely
been driven by computational scale rather than insights from biological intelligence. Here …

Primates, including humans, can typically recognize objects in visual images at a glance
despite naturally occurring identity-preserving image transformations (eg, changes in …

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

The internal representations of early deep artificial neural networks (ANNs) were found to be
remarkably similar to the internal neural representations measured experimentally in the …

Deep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as
a modeling framework for neural computations in the primate brain. Just like individual …

Deep convolutional neural networks (DCNNs) and the ventral visual pathway share vast
architectural and functional similarities in visual challenges such as object recognition …

How do we recognize objects despite changes in their appearance? The past three decades
have been witness to intense debates regarding both whether objects are encoded …

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 …

Achievement of human-level image recognition by deep neural networks (DNNs) has
spurred interest in whether and how DNNs are brain-like. Both DNNs and the visual cortex …