English(EN) One Loop, Two Gains: Can Active Learning win the Lottery for Free?

新方法将剪枝整合到主动学习中以寻找稀疏模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一种名为“改进与剪枝”（I&P）的新方法，该方法将幅度剪枝整合到主动学习再训练周期中。该方法旨在主动学习流程中发现稀疏子网络或“中奖彩票”，而无需显著增加计算成本。研究结果表明，I&P 可以在每次主动学习迭代中生成可部署的稀疏模型，在高达 95% 的稀疏度下实现与密集模型相当的准确性。该方法解决了再训练和获取评分中的计算瓶颈，有望使主动学习在更大模型和数据集上得到实际应用。 AI

影响这项研究可能有助于在主动学习场景中更有效地训练和部署稀疏模型，从而降低计算成本并提高可扩展性。

排序理由该集群包含一篇详细介绍机器学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 8 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍机器学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Benedikt Tscheschner, Eduardo Veas, Marc Masana · 2026-09-10 04:00

一环双赢：主动学习能否为免费赢得彩票？

arXiv:2609.10311v1 Announce Type: cross Abstract: The lottery ticket hypothesis posits the existence of winning tickets: sparse subnetworks that, when trained in isolation from their original initialization, match the accuracy of the full dense network. The predominant method for…

本文如何被排名

报道来源 [1]

一环双赢：主动学习能否为免费赢得彩票？

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