Evaluating and Crafting Datasets Effective for Deep Learning With Data Maps
This addresses the challenge of dataset quality and efficiency in supervised learning, particularly for reducing manual labeling and computational costs, though it appears incremental as it builds on existing data curation techniques.
The paper tackles the problem of excessive resource and time requirements for training deep learning models on large datasets by proposing a method to curate smaller datasets that maintain comparable out-of-distribution accuracy, using a distribution of samples based on learning difficulty.
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.