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Modeling biological face recognition with deep convolutional neural networks
In this review, we summarize the first studies that use DCNNs to model biological face recognition.
Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movements
Deep Convolutional Neural Networks (DCNNs) were originally inspired by principles of biological vision, have evolved into best current computational models of object recognition, and consequently indicate strong architectural and functional parallelism with the ventral visual pathway throughout comparisons with neuroimaging and neural time series data.
Comparing object recognition in humans and deep convolutional neural networks -- An eye tracking study
Deep convolutional neural networks (DCNNs) and the ventral visual pathway share vast architectural and functional similarities in visual challenges such as object recognition.
Seeing eye-to-eye? A comparison of object recognition performance in humans and deep convolutional neural networks under image manipulation
This study aims towards a behavioral comparison of visual core object recognition performance between humans and feedforward neural networks in a classification learning paradigm on an ImageNet data set.
