2026-04-14T08:13:34Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/560012025-12-18T01:25:25Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Gomez-Villa, Alex author Martín, Adrian author Vazquez-Corral, Javier author Bertalmío, Marcelo author Malo, Jesús author 2023-03-01T13:51:55Z 2023-03-01T13:51:55Z 2022 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. visual illusions synthesis of stimuli visual response models image distortion metrics deep generative models generative adversarial networks On the synthesis of visual illusions using deep generative models