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动态图像难度感知 DNN 修剪
Vasileios Pentsos1, Ourania Spantidi1, Iraklis Anagnostopoulos1
1School of Electrical, Computer and Biomedical Engineering, Southern Illinois University, Carbondale, IL 62901, USA.
Micromachines
|May 27, 2023
概括
本研究介绍了动态深度神经网络 (DNN) 修剪,根据图像难度调整模型复杂性. 这种方法有效地减少了对资源有限的设备的模型尺寸和操作,而无需重新培训.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 在图像识别方面表现出色,但由于模型大小,阻碍了在资源有限的设备上部署.
- 在边缘设备和移动平台上高效地部署DNN仍然是机器学习研究中的重大挑战.
研究的目的:
- 为深度神经网络 (DNN) 提出一种新的动态修剪方法,该方法在推断过程中根据输入图像的复杂性调整修剪.
- 为了减少DNN模型的计算负载和内存足迹,而不会影响性能.
主要方法:
- 开发了一个动态DNN修剪策略,该策略根据输入图像的感知难度动态调整模型的复杂性.
- 在使用各种最先进的DNN架构对ImageNet数据集进行了评估.
- 评估了通过动态修剪技术实现的模型尺寸和计算操作 (例如,FLOP) 的减少.
主要成果:
- 动态修剪方法显著减少了DNN模型大小和推断所需的操作数量.
- 该方法在多个最先进的DNN中表现出有效性,而不需要重新训练或微调模型.
- 实现了显著的效率提升,使得DNN更适合于资源有限的环境.
结论:
- 提出的动态DNN修剪方法为创建轻量级和自适应DNN模型提供了有效的解决方案.
- 这种方法可以在具有有限计算能力和内存的设备上高效地部署先进的图像识别功能.
- 动态修剪为高效深度学习框架的未来研究提供了一个有希望的方向.
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