  The customizable nature of deep learning models have allowed them to be
successful predictors in various disciplines. These models are often trained
with respect to thousands or millions of instances for complicated problems,
but the gathering of such an immense collection may be infeasible and
expensive. However, what often occurs is the pollution of redundant information
from these instances to the deep learning models. This paper outlines an
algorithm that dynamically selects and appends instances to a training dataset
from uncertain regions of the parameter space based on differences in
predictions from multiple convolutional neural networks (CNNs). These CNNs are
also simultaneously trained on this growing dataset to construct more accurate
and knowledgable models. The methodology presented has reduced training dataset
sizes by almost 90% and maintained predictive power in two diagnostics of high
energy density physics.
