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Area of Science:

  • Computational Neuroscience
  • Computer Vision
  • Psychophysics

Background:

  • Visual illusions provide critical constraints for understanding the human visual system.
  • Two strategies exist: matching human illusions in models or synthesizing illusions from models.

Purpose of the Study:

  • To explore the less-utilized strategy of synthesizing visual illusions from computational models.
  • To develop a framework for generating novel visual illusions using automatic differentiation.

Main Methods:

  • Proposed a framework leveraging automatic differentiation for illusion synthesis.
  • Validated the framework with psychophysical experiments.
  • Applied the framework to both classical and artificial neural network vision models.

Main Results:

  • Demonstrated a method to synthesize compelling visual illusions from computational vision models.
  • The framework successfully generated novel illusions.
  • Psychophysical validation confirmed the illusions' effectiveness.

Conclusions:

  • The developed framework enables the creation of new visual illusions from AI models.
  • This approach facilitates the study of discrepancies between AI and human visual perception.
  • The framework can optimize artificial vision models to better align with human perception.