Convolutional neural networks (CNNs) were inspired by early findings in the study of
biological vision. They have since become successful tools in computer vision and state-of …

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

Widely observed neural scaling laws, in which error falls off as a power of the training set
size, model size, or both, have driven substantial performance improvements in deep …

A few years ago, the first CNN surpassed human performance on ImageNet. However, it
soon became clear that machines lack robustness on more challenging test cases, a major …

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 …

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 …

What might sound like the beginning of a joke has become an attractive prospect for many
cognitive scientists: the use of deep neural network models (DNNs) as models of human …

Convolutional Neural Networks (CNN) are a class of machine learning models
predominately used in computer vision tasks and can achieve human-like performance …

Networks of interconnected neurons communicating through spiking signals offer the
bedrock of neural computations. Our brain's spiking neural networks have the computational …

Advanced computer vision mechanisms have been inspired by neuroscientific findings.
However, with the focus on improving benchmark achievements, technical solutions have …