“Designing Perceptual Puzzles by Differentiating Probabilistic Programs” by Chandra, Li, Tenenbaum and Ragan-Kelley

  • ©Kartik Chandra, Tzu-Mao Li, Joshua Tenenbaum, and Jonathan Ragan-Kelley



    Designing Perceptual Puzzles by Differentiating Probabilistic Programs

Program Title:

    Demo Labs



    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.


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