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 …

In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …

Deep neural networks (DNNs) have had extraordinary successes in classifying
photographic images of objects and are often described as the best models of biological …

Photos shared by tourists are being generated at an unprecedented speed, creating new
opportunities to study tourism destination images. Nevertheless, little research has focused …

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 …

Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in
spiking neural networks (SNN) to extract visual features of low or intermediate complexity in …

Artificial vision has often been described as one of the key remaining challenges to be
solved before machines can act intelligently. Recent developments in a branch of machine …

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 …

Stimuli are represented in the brain by the collective population responses of sensory
neurons, and an object presented under varying conditions gives rise to a collection of …

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …