  Meaningful uncertainty quantification in computer vision requires reasoning
about semantic information -- say, the hair color of the person in a photo or
the location of a car on the street. To this end, recent breakthroughs in
generative modeling allow us to represent semantic information in disentangled
latent spaces, but providing uncertainties on the semantic latent variables has
remained challenging. In this work, we provide principled uncertainty intervals
that are guaranteed to contain the true semantic factors for any underlying
generative model. The method does the following: (1) it uses quantile
regression to output a heuristic uncertainty interval for each element in the
latent space (2) calibrates these uncertainties such that they contain the true
value of the latent for a new, unseen input. The endpoints of these calibrated
intervals can then be propagated through the generator to produce interpretable
uncertainty visualizations for each semantic factor. This technique reliably
communicates semantically meaningful, principled, and instance-adaptive
uncertainty in inverse problems like image super-resolution and image
completion.
