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On the synthesis of visual illusions using deep generative models
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 c...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Association for Research in Vision and Ophthalmology
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9290318/ https://www.ncbi.nlm.nih.gov/pubmed/35833884 http://dx.doi.org/10.1167/jov.22.8.2 |
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author | Gomez-Villa, Alex Martín, Adrián Vazquez-Corral, Javier Bertalmío, Marcelo Malo, Jesús |
author_facet | Gomez-Villa, Alex Martín, Adrián Vazquez-Corral, Javier Bertalmío, Marcelo Malo, Jesús |
author_sort | Gomez-Villa, Alex |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9290318 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Association for Research in Vision and Ophthalmology |
record_format | MEDLINE/PubMed |
spelling | pubmed-92903182022-07-19 On the synthesis of visual illusions using deep generative models Gomez-Villa, Alex Martín, Adrián Vazquez-Corral, Javier Bertalmío, Marcelo Malo, Jesús J Vis Article 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. The Association for Research in Vision and Ophthalmology 2022-07-14 /pmc/articles/PMC9290318/ /pubmed/35833884 http://dx.doi.org/10.1167/jov.22.8.2 Text en Copyright 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
spellingShingle | Article Gomez-Villa, Alex Martín, Adrián Vazquez-Corral, Javier Bertalmío, Marcelo Malo, Jesús On the synthesis of visual illusions using deep generative models |
title | On the synthesis of visual illusions using deep generative models |
title_full | On the synthesis of visual illusions using deep generative models |
title_fullStr | On the synthesis of visual illusions using deep generative models |
title_full_unstemmed | On the synthesis of visual illusions using deep generative models |
title_short | On the synthesis of visual illusions using deep generative models |
title_sort | on the synthesis of visual illusions using deep generative models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9290318/ https://www.ncbi.nlm.nih.gov/pubmed/35833884 http://dx.doi.org/10.1167/jov.22.8.2 |
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