Core Concepts
Balancing computational efficiency and model accuracy through coreset selection and data reweighting.
Abstract
Introduction to the challenges in machine learning with large datasets.
Proposal of a novel method combining coreset selection and data reweighting.
Detailed explanation of the methodology including coreset selection, reweighting, and weight broadcasting.
Experimental results showcasing the effectiveness of the proposed method on CIFAR-10 and CIFAR-100 datasets.
Comparison with existing methods like ERM, W-ERM, CR-ERM, and CMS-ERM.
Stats
Less than 1% of the dataset is sufficient for effective reweighting.
Our proposed CW-ERM achieves average accuracies of 94.9% on CIFAR-10 and 76.7% on CIFAR-100 datasets.
Quotes
"Our approach not only addresses the limitations of each individual method but also synergistically enhances their strengths."
"Our proposed method not only quickens the pace of machine learning tasks but also elevates their accuracy."
"Our experiments substantiate that CW-ERM offers a robust and efficient strategy for machine learning tasks."