English(EN) Rethinking Dataset Distillation for Classification: Do Distilled Sets Outperform Coresets?

新研究表明数据集蒸馏效果不如核心集选择

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-16 17:37

一篇新的研究论文批判性地评估了数据集蒸馏（DD）方法，发现它们通常不如更简单的核心集选择（CS）策略，尤其是在ImageNet等大规模数据集上。该研究将七种最先进的DD方法与三种CS策略进行了基准测试，结果显示DD方法的计算成本可能更高，且实际优势有限。核心集还能更好地覆盖原始数据分布。 AI

影响表明对于许多大规模机器学习任务，像核心集选择这样更简单、更有效的方法可能比数据集蒸馏更可取。

排序理由该集群包含一篇在arXiv上发表的评估机器学习技术的论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Trisha Mittal, Akshay Mehra, Joshua Kimball · 2026-06-17 04:00

重新思考分类数据集蒸馏：蒸馏集是否优于核集？

arXiv:2606.18209v1 Announce Type: new Abstract: Dataset distillation (DD) has emerged as a prominent approach in data centric machine learning, aiming to synthesize compact training sets for efficient training by compressing the information in large datasets into a small number o…
arXiv cs.LG TIER_1 English(EN) · Joshua Kimball · 2026-06-16 17:37

重新思考分类数据集蒸馏：蒸馏集是否优于核心集？

Dataset distillation (DD) has emerged as a prominent approach in data centric machine learning, aiming to synthesize compact training sets for efficient training by compressing the information in large datasets into a small number of synthetic samples. However, DD methods are oft…