  Humans are accustomed to environments that contain both regularities and
exceptions. For example, at most gas stations, one pays prior to pumping, but
the occasional rural station does not accept payment in advance. Likewise, deep
neural networks can generalize across instances that share common patterns or
structures, yet have the capacity to memorize rare or irregular forms. We
analyze how individual instances are treated by a model via a consistency
score. The score characterizes the expected accuracy for a held-out instance
given training sets of varying size sampled from the data distribution. We
obtain empirical estimates of this score for individual instances in multiple
data sets, and we show that the score identifies out-of-distribution and
mislabeled examples at one end of the continuum and strongly regular examples
at the other end. We identify computationally inexpensive proxies to the
consistency score using statistics collected during training. We show examples
of potential applications to the analysis of deep-learning systems.
