  We consider the problem of combining machine learning models to perform
higher-level cognitive tasks with clear specifications. We propose the novel
problem of Visual Discrimination Puzzles (VDP) that requires finding
interpretable discriminators that classify images according to a logical
specification. Humans can solve these puzzles with ease and they give robust,
verifiable, and interpretable discriminators as answers. We propose a
compositional neurosymbolic framework that combines a neural network to detect
objects and relationships with a symbolic learner that finds interpretable
discriminators. We create large classes of VDP datasets involving natural and
artificial images and show that our neurosymbolic framework performs favorably
compared to several purely neural approaches.
