# Data Pruning via Separability, Integrity, and Model Uncertainty-Aware Importance Sampling

Source: [Data Pruning via Separability, Integrity, and Model Uncertainty-Aware Importance Sampling](<https://www.sota2.com/research/paper/68fa74e91e209131cea64a94>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:21:26.418Z

Authors: Steven Grosz, Rui Zhao, Rajeev Ranjan, Hongcheng Wang, Manoj Aggarwal, Gerard Medioni, Anil Jain

Published: 2024-09-20T21:45:02.000Z

## Abstract

This paper improves upon existing data pruning methods for image classification by introducing a novel pruning metric and pruning procedure based on importance sampling. The proposed pruning metric explicitly accounts for data separability, data integrity, and model uncertainty, while the sampling procedure is adaptive to the pruning ratio and considers both intra-class and inter-class separation to further enhance the effectiveness of pruning. Furthermore, the sampling method can readily be applied to other pruning metrics to improve their performance. Overall, the proposed approach scales well to high pruning ratio and generalizes better across different classification models, as demonstrated by experiments on four benchmark datasets, including the fine-grained classification scenario.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2409.13915>)

[PDF](<https://arxiv.org/pdf/2409.13915.pdf>)