由单一值驱动的自动过器修剪
Van Tien Pham1, Yassine Zniyed1, Thanh Phuong Nguyen2
1Université de Toulon, Aix Marseille University, CNRS, LIS, UMR 7020, France.
概括
我们介绍了SLIMING,一种使用单数值的自动过器修剪方法,以优化神经网络过器. 这种方法使复杂的修剪简化为两个步骤,保持过器结构以提高效率.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 自动过器修剪对于高效的深度学习模型至关重要.
- 现有的方法在优化波器张量和保存多维结构方面面临着挑战.
研究的目的:
- 为了介绍SLIMING (单个值驱动的自动镜修剪),一种新的自动镜修剪方法.
- 通过提出两步优化流程来解决过器修剪中的组合性挑战.
主要方法:
- SLIMING将过器修剪形式化为使用单数值的优化问题.
- 建议采用两步方法: (i) 确定修剪配置和 (ii) 选择过器.
- 为每个步骤开发了简单的算法,确保了过器多维结构的保存.
主要成果:
- 在合成玩具示例上的数值模拟验证了这一方法.
- 在八个架构,四个数据集和四个视觉任务中进行了广泛的模拟,证明了框架的有效性.
结论:
- SLIMING为自动过器修剪提供了一种有效和结构化的方法.
- 该方法成功地保持了波器的多维性,提高了模型的效率和性能.
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