On the synthesis of visual illusions using deep generative models

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  • dc.contributor.author Gomez-Villa, Alex
  • dc.contributor.author Martín, Adrian
  • dc.contributor.author Vazquez-Corral, Javier
  • dc.contributor.author Bertalmío, Marcelo
  • dc.contributor.author Malo, Jesús
  • dc.date.accessioned 2023-03-01T13:51:55Z
  • dc.date.available 2023-03-01T13:51:55Z
  • dc.date.issued 2022
  • dc.description.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.
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Gomez-Villa A, Martín A, Vazquez-Corral J, Bertalmío M, Malo J. On the synthesis of visual illusions using deep generative models. J Vis. 2022;22(8):2. DOI: 10.1167/jov.22.8.2
  • dc.identifier.doi http://dx.doi.org/10.1167/jov.22.8.2
  • dc.identifier.issn 1534-7362
  • dc.identifier.uri http://hdl.handle.net/10230/56001
  • dc.language.iso eng
  • dc.publisher Association for Research in Vision and Ophthalmology (ARVO)
  • dc.relation.ispartof Journal of Vision. 2022;22(8):2.
  • dc.rights Copyright 2022 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
  • dc.subject.keyword visual illusions
  • dc.subject.keyword synthesis of stimuli
  • dc.subject.keyword visual response models
  • dc.subject.keyword image distortion metrics
  • dc.subject.keyword deep generative models
  • dc.subject.keyword generative adversarial networks
  • dc.title On the synthesis of visual illusions using deep generative models
  • dc.type info:eu-repo/semantics/article
  • dc.type.version info:eu-repo/semantics/publishedVersion