Abstract Deep Convolutional Neural Networks (DNNs) have achieved superhuman
accuracy on standard image classification benchmarks. Their success has reignited …

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 …

Despite impressive performance on numerous visual tasks, Convolutional Neural Networks
(CNNs)---unlike brains---are often highly sensitive to small perturbations of their input, eg …

Deep neural networks (DNNs) for object classification have been argued to provide the most
promising model of the visual system, accompanied by claims that they have attained or …

Deep neural networks set the state-of-the-art across many tasks in computer vision, but their
generalization ability to simple image distortions is surprisingly fragile. In contrast, the …

Deep convolutional neural networks (DCNNs) have revolutionized computer vision and are
often advocated as good models of the human visual system. However, there are currently …

Deep neural networks (DNNs) have revolutionized computer science and are now widely
used for neuroscientific research. A hot debate has ensued about the usefulness of DNNs as …

The ultimate goal of neuroscience is to explain how complex behaviour arises from neuronal
activity. A comparable level of complexity also emerges in deep neural networks (DNNs) …

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 …