Designing Perceptual Puzzles by Differentiating Probabilistic Programs

Abstract

We design new visual illusions by finding "adversarial examples" for principled models of human perception -- specifically, for probabilistic models, which treat vision as Bayesian inference. To perform this search efficiently, we design a differentiable probabilistic programming language, whose API exposes MCMC inference as a first-class differentiable function. We demonstrate our method by automatically creating illusions for three features of human vision: color constancy, size constancy, and face perception.

9 pages; 3 figures; SIGGRAPH '22 Conference Proceedings

References in corpus (3)

• Gradient-based Hyperparameter Optimization through Reversible Learning
• Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
• LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models