ArticleJournal of vision2021
ImageNet-trained deep neural networks exhibit illusion-like response to the Scintillating grid.
Article in Journal of vision, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography.BMC oral health · 2025Article
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- On traveling wave solutions with stability and phase plane analysis for the modified Benjamin-Bona-Mahony equation.PloS one · 2024Article
- Article
- Reconstructing visual illusory experiences from human brain activity.Science advances · 2023Article
- Challenging deep learning models with image distortion based on the abutting grating illusion.Patterns (New York, N.Y.) · 2023Article
- Motion illusion-like patterns extracted from photo and art images using predictive deep neural networks.Scientific reports · 2022Article
- A comparative biology approach to DNN modeling of vision: A focus on differences, not similarities.Journal of vision · 2021Article
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2 authors.
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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.
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