19 citations · 30 across the 4 of their papers we have counts for
4 papers
Modeling biological face recognition with deep convolutional neural networks
Leonard E. van Dyck, Walter R. Gruber
Deep convolutional neural networks (DCNNs) have become the state-of-the-art computational models of biological object recognition. Their remarkable success has helped vision scienc…
Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movements
Leonard E. van Dyck, Sebastian J. Denzler, Walter R. Gruber
Deep Convolutional Neural Networks (DCNNs) were originally inspired by principles of biological vision, have evolved into best current computational models of object recognition, a…
Comparing object recognition in humans and deep convolutional neural networks -- An eye tracking study
Leonard E. van Dyck, Roland Kwitt, Sebastian J. Denzler +1
Deep convolutional neural networks (DCNNs) and the ventral visual pathway share vast architectural and functional similarities in visual challenges such as object recognition. Rece…
Seeing eye-to-eye? A comparison of object recognition performance in humans and deep convolutional neural networks under image manipulation
Leonard E. van Dyck, Walter R. Gruber
For a considerable time, deep convolutional neural networks (DCNNs) have reached human benchmark performance in object recognition. On that account, computational neuroscience and…