  Ensembling multiple predictions is a widely used technique for improving the
accuracy of various machine learning tasks. One obvious drawback of ensembling
is its higher execution cost during inference. In this paper, we first describe
our insights on the relationship between the probability of prediction and the
effect of ensembling with current deep neural networks; ensembling does not
help mispredictions for inputs predicted with a high probability even when
there is a non-negligible number of mispredicted inputs. This finding motivated
us to develop a way to adaptively control the ensembling. If the prediction for
an input reaches a high enough probability, i.e., the output from the softmax
function, on the basis of the confidence level, we stop ensembling for this
input to avoid wasting computation power. We evaluated the adaptive ensembling
by using various datasets and showed that it reduces the computation cost
significantly while achieving accuracy similar to that of static ensembling
using a pre-defined number of local predictions. We also show that our
statistically rigorous confidence-level-based early-exit condition reduces the
burden of task-dependent threshold tuning better compared with naive early exit
based on a pre-defined threshold in addition to yielding a better accuracy with
the same cost.
