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.
@article{arxiv.2409.13915,
title = {Data Pruning via Separability, Integrity, and Model Uncertainty-Aware Importance Sampling},
author = {Steven Grosz and Rui Zhao and Rajeev Ranjan and Hongcheng Wang and Manoj Aggarwal and Gerard Medioni and Anil Jain},
journal= {arXiv preprint arXiv:2409.13915},
year = {2024}
}