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• W3205187079 startingPage "15" @default.
• W3205187079 abstract "Deep neural network (DNN) models for computer vision are capable of human-level object recognition. Consequently, similarities between DNN and human vision are of interest. Here, we characterize DNN representations of Scintillating grid visual illusion images in which white disks are perceived to be partially black. Specifically, we use VGG-19 and ResNet-101 DNN models that were trained for image classification and consider the representational dissimilarity ((L^1) distance in the penultimate layer) between pairs of images: one with white Scintillating grid disks and the other with disks of decreasing luminance levels. Results showed a nonmonotonic relation, such that decreasing disk luminance led to an increase and subsequently a decrease in representational dissimilarity. That is, the Scintillating grid image with white disks was closer, in terms of the representation, to images with black disks than images with gray disks. In control nonillusion images, such nonmonotonicity was rare. These results suggest that nonmonotonicity in a deep computational representation is a potential test for illusion-like response geometry in DNN models." @default.
• W3205187079 created "2021-10-25" @default.
• W3205187079 creator A5089453761 @default.
• W3205187079 creator A5090360853 @default.
• W3205187079 date "2021-10-22" @default.
• W3205187079 modified "2023-10-14" @default.
• W3205187079 title "ImageNet-trained deep neural networks exhibit illusion-like response to the Scintillating grid" @default.
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• W3205187079 doi "https://doi.org/10.1167/jov.21.11.15" @default.
• W3205187079 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8543405" @default.
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• W3205187079 hasPublicationYear "2021" @default.
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