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arXiv · 2411.12115

Distill the Best, Ignore the Rest: Improving Dataset Distillation with Loss-Value-Based Pruning

Brian B. Moser·Federico Raue·Tobias C. Nauen·Stanislav Frolov·Andreas Dengel
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Abstract

Dataset distillation has gained significant interest in recent years, yet existing approaches typically distill from the entire dataset, potentially including non-beneficial samples. We introduce a novel "Prune First, Distill After" framework that systematically prunes datasets via loss-based sampling prior to distillation. By leveraging pruning before classical distillation techniques and generative priors, we create a representative core-set that leads to enhanced generalization for unseen architectures - a significant challenge of current distillation methods. More specifically, our proposed framework significantly boosts distilled quality, achieving up to a 5.2 percentage points accuracy increase even with substantial dataset pruning, i.e., removing 80% of the original dataset prior to distillation. Overall, our experimental results highlight the advantages of our easy-sample prioritization and cross-architecture robustness, paving the way for more effective and high-quality dataset distillation.

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Brian B. Moser, Federico Raue, Tobias C. Nauen, Stanislav Frolov, Andreas Dengel. 2024-11-18. Distill the Best, Ignore the Rest: Improving Dataset Distillation with Loss-Value-Based Pruning. https://doi.org/10.1109/ijcnn64981.2025.11229108.

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