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Rapid development in deep learning model construction has prompted anincreased need for appropriate training data. The popularity of large datasets- sometimes known as "big data" - has diverted attention from assessing theirquality. Training on large datasets often requires excessive system resourcesand an infeasible amount of time. Furthermore, the supervised machine learningprocess has yet to be fully automated: for supervised learning, large datasetsrequire more time for manually labeling samples. We propose a method ofcurating smaller datasets with comparable out-of-distribution model accuracyafter an initial training session using an appropriate distribution of samplesclassified by how difficult it is for a model to learn from them.

Evaluating and Crafting Datasets Effective for Deep Learning With Data Maps