Gérard Medioni

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

2024 · arXiv (Cornell University)

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, …