REDUCR: Robust Data Downsampling Using Class Priority Reweighting

William Bankes; George Hughes; Ilija Bogunovic; Zi Wang

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arXiv · 2312.00486

REDUCR: Robust Data Downsampling Using Class Priority Reweighting

William Bankes·George Hughes·Ilija Bogunovic·Zi Wang

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Abstract

Modern machine learning models are becoming increasingly expensive to train for real-world image and text classification tasks, where massive web-scale data is collected in a streaming fashion. To reduce the training cost, online batch selection techniques have been developed to choose the most informative datapoints. However, these techniques can suffer from poor worst-class generalization performance due to class imbalance and distributional shifts. This work introduces REDUCR, a robust and efficient data downsampling method that uses class priority reweighting. REDUCR reduces the training data while preserving worst-class generalization performance. REDUCR assigns priority weights to datapoints in a class-aware manner using an online learning algorithm. We demonstrate the data efficiency and robust performance of REDUCR on vision and text classification tasks. On web-scraped datasets with imbalanced class distributions, REDUCR significantly improves worst-class test accuracy (and average accuracy), surpassing state-of-the-art methods by around 15%.

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BibTeX RIS

William Bankes, George Hughes, Ilija Bogunovic, Zi Wang. 2024-11-25. REDUCR: Robust Data Downsampling Using Class Priority Reweighting. https://arxiv.org/abs/2312.00486

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