We describe and analyze the performance of metric learning systems, including deep neural networks (DNNs), on a new dataset of human visual object shape similarity judgments of naturalistic, part-based objects known as "Fribbles". In contrast to prev...
Feedforward Convolutional Neural Networks (ffCNNs) have become state-of-the-art models both in computer vision and neuroscience. However, human-like performance of ffCNNs does not necessarily imply human-like computations. Previous studies have sugge...
Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore h...