Deep neural networks (DNNs) achieve excellent performance on standard classification
tasks. However, under image quality distortions such as blur and noise, classification …

Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and
natural language processing via recurrent neural networks (RNNs). However, success …

The recent success of Vision Transformers is shaking the long dominance of Convolutional
Neural Networks (CNNs) in image recognition for a decade. Specifically, in terms of …

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 …

Convolutional neural networks (CNNs) are often described as promising models of human
vision, yet they show many differences from human abilities. We focus on a superhuman …

In the past decade, deep neural networks, and specifically convolutional neural networks
(CNNs), have been becoming a primary tool in the field of biomedical image analysis, and …

Unit activity in particular deep neural networks (DNNs) are remarkably similar to the
neuronal population responses to static images along the primate ventral visual cortex …

Deep neural networks have recently shown impressive classification performance on a
diverse set of visual tasks. When deployed in real-world (noise-prone) environments, it is …

Convolutional neural networks are a class of deep neural networks that leverage spatial
information, and they are therefore well suited to classifying images for a range of …

In recent years, convolutional neural networks (CNNs) have been applied successfully in
many fields. However, these deep neural models are still considered as “black box” for most …