  Rapid development in deep learning model construction has prompted an
increased need for appropriate training data. The popularity of large datasets
- sometimes known as "big data" - has diverted attention from assessing their
quality. Training on large datasets often requires excessive system resources
and an infeasible amount of time. Furthermore, the supervised machine learning
process has yet to be fully automated: for supervised learning, large datasets
require more time for manually labeling samples. We propose a method of
curating smaller datasets with comparable out-of-distribution model accuracy
after an initial training session using an appropriate distribution of samples
classified by how difficult it is for a model to learn from them.
