English(EN) Decision-FLIP: Budget-Free Structured Pruning for Gaussian Occupancy Models

Decision-FLIP框架为高斯占用模型实现无预算结构化剪枝

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员开发了Decision-FLIP，一种用于剪枝高斯占用模型的新颖框架，无需预定义预算。该方法使用无标签的校准输入来评分和选择要删除的行，旨在提高模型效率。Decision-FLIP已证明在模型大小和延迟方面有显著降低，合格行节省高达79%，延迟节省高达45%，同时在主要终点上保持高准确性。 AI

影响这项研究通过在不牺牲性能的情况下减小模型尺寸和延迟，可能带来更高效的AI模型。

排序理由这是一篇详细介绍新模型剪枝方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Jingxing Li, Anurag Paul, Subhasis Das, Cindy Xu · 2026-10-16 04:00

Decision-FLIP：高斯占用模型的无预算结构化剪枝

arXiv:2610.20917v1 Announce Type: new Abstract: Occupancy predictors rely on large persistent banks of Gaussians or learned queries whose size is fixed by hand. Common pruning proxies, such as opacity, activation frequency, or parameter magnitude, do not measure whether removing …

本文如何被排名

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Decision-FLIP：高斯占用模型的无预算结构化剪枝

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