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 …

Our mind can represent various objects from physical world in an abstract and complex high-
dimensional object space, with axes encoding critical features to quickly and accurately …

Visual translation tolerance refers to our capacity to recognize objects over a wide range of
different retinal locations. Although translation is perhaps the simplest spatial transform that …

Recent gains in model robustness towards out-of-distribution images are predominantly
achieved through ever-increasing large-scale datasets. While this approach is very effective …

Humans are successfully able to recognize objects in a variety of image distributions.
Today's artificial neural networks (ANNs), on the other hand, struggle to recognize objects in …

Recent advances in machine learning have made real-world perception tasks feasible for
computers, in many cases approaching levels of performance similar to those of humans. In …

Invariant object recognition, a cornerstone of human vision, enables recognizing objects
despite variations in rotations, positions, and scales. To emulate human-like generalization …

This paper explores the automated recognition of objects and materials and their relation to
depictions in images of all kinds: photographs, artwork, doodles by children, and any other …

Primate inferotemporal cortex (IT) has been linked to the remarkable human ability of visual
object recognition. The linear linkage hypothesis posits that a linear readout of IT neural …