ArticleJournal of vision2022
On the synthesis of visual illusions using deep generative models.
Article in Journal of vision, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026Review
- MAME: Multidimensional adaptive metamer exploration with human perceptual feedback.Journal of vision · 2026Article
- A deep convolutional neural network trained for lightness constancy is susceptible to lightness illusions.Journal of vision · 2026Article
- A novel visual illusion paradigm provides evidence for a general factor of illusion sensitivity and personality correlates.Scientific reports · 2023Article
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Authors and funding
5 authors.
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Abstract
Visual illusions expand our understanding of the visual system by imposing constraints in the models in two different ways: i) visual illusions for humans should induce equivalent illusions in the model, and ii) illusions synthesized from the model should be compelling for human viewers too. These constraints are alternative strategies to find good vision models. Following the first research strategy, recent studies have shown that artificial neural network architectures also have human-like illusory percepts when stimulated with classical hand-crafted stimuli designed to fool humans. In this work we focus on the second (less explored) strategy: we propose a framework to synthesize new visual illusions using the optimization abilities of current automatic differentiation techniques. The proposed framework can be used with classical vision models as well as with more recent artificial neural network architectures. This framework, validated by psychophysical experiments, can be used to study the difference between a vision model and the actual human perception and to optimize the vision model to decrease this difference.
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