Evaluating and Crafting Datasets Effective for Deep Learning With Data Maps

• URL: http://arxiv.org/abs/2208.10033v1
• Date: Mon, 22 Aug 2022 03:30:18 GMT
• Title: Evaluating and Crafting Datasets Effective for Deep Learning With Data Maps
• Authors: Jay Bishnu and Andrew Gondoputro
• Abstract summary: Training on large datasets often requires excessive system resources and an infeasible amount of time. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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