English(EN) BaCP: Backbone Contrastive Pruning for Preserving Representations in Extremely Sparse Neural Networks

新的剪枝方法在极度稀疏的情况下保留神经网络表示

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员开发了骨干对比剪枝（BaCP）方法，该方法旨在在经过极端非结构化剪枝的神经网络中保持表示准确性。BaCP通过将稀疏网络的嵌入空间与各种参考模型对齐，包括预训练、微调和历史快照模型。这种方法基于CAP框架的对比分解，在许多场景下，尤其是在传统剪枝方法失效的极端稀疏场景下，都显示出显著的准确性提升。 AI

影响该方法通过允许在不显著损失准确性的情况下实现极度稀疏，从而能够更有效地部署大型神经网络。

排序理由该集群包含一篇详细介绍新的神经网络剪枝方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Mohammad Haroon Khawaja, Muhammad Haseeb, Mohammad Fatim Shoaib, Muhammad Tahir · 2026-10-05 04:00

BaCP：骨干对比剪枝用于极稀疏神经网络中保留表示

arXiv:2610.02524v1 Announce Type: new Abstract: Unstructured pruning at extreme sparsity often suffers from representational collapse, causing sharp drops in accuracy. To address this, we study Backbone Contrastive Pruning (BaCP), which regularizes the sparse network's embedding …

本文如何被排名

报道来源 [1]

BaCP：骨干对比剪枝用于极稀疏神经网络中保留表示

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