Reflecting After Learning for Understanding
  Today, image classification is a common way for systems to process visual
content. Although neural network approaches to classification have seen great
progress in reducing error rates, it is not clear what this means for a
cognitive system that needs to make sense of the multiple and competing
predictions from its own classifiers. As a step to address this, we present a
novel framework that uses meta-reasoning and meta-operations to unify
predictions into abstractions, properties, or relationships. Using the
framework on images from ImageNet, we demonstrate systems that unify 41% to 46%
of predictions in general and unify 67% to 75% of predictions when the systems
can explain their conceptual differences. We also demonstrate a system in "the
wild" by feeding live video images through it and show it unifying 51% of
predictions in general and 69% of predictions when their differences can be
explained conceptually by the system. In a survey given to 24 participants, we
found that 87% of the unified predictions describe their corresponding images.
