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通过张量分解和修剪来增强网络压缩
概括
我们介绍了NORTON,这是一个结合张量分解和修剪的网络压缩方法. 诺顿通过使用过器分解和结构化修剪来提高模型的效率和准确性.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 网络压缩对于在资源有限的设备上部署深度学习模型至关重要.
- 结合张量分解和修剪,为网络压缩提供了协同效益.
- 现有的方法可能无法充分利用张量分解和修剪的优势.
研究的目的:
- 为了提出一种新的网络压缩方法,NORTON (通过TensOr分解和 pruNing进行网络cOmpRession).
- 通过整合过器分解与结构化修剪来增强网络压缩.
- 为了证明NORTON在各种网络架构和任务中的有效性.
主要方法:
- 诺顿使用过器分解来详细分析网络重量.
- 一种新的结构化修剪方法与分解模型相集成.
- 在不同的架构,数据集和计算机视觉任务上进行了实验.
主要成果:
- 与最先进的 (SOTA) 压缩技术相比,NORTON 实现了更高的性能.
- 该方法在模型复杂性降低方面取得了显著的改进.
- 精度保持或提高,展示了压缩策略的有效性.
结论:
- 诺顿通过结合张量分解和修剪,提供了一种有效的网络压缩方法.
- 拟议的过器分解和结构化修剪集成带来了显著的好处.
- 诺顿为有效的深度学习模型部署做出了宝贵的贡献.
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