REAF:记忆增强和基于的非对称忘记用于过器修剪
概括
这项研究介绍了记忆增强和基于的非对称遗忘 (REAF),一种新的过器修剪方法. REAF通过改善记忆保留和逐渐忘记信息来增强模型压缩,以最小的准确性损失实现卓越的性能.
科学领域:
- 深度学习是一种深度学习.
- 计算机视觉 计算机视觉 计算机视觉
- 模型的压缩压缩.
背景情况:
- 过器修剪对于高效的深度学习模型至关重要.
- 由于过早忘记,现有的方法往往会导致不可逆转的信息丢失和低于最佳的性能.
- 基线模型的性能可以限制削减网络的潜力.
研究的目的:
- 开发一种新的过器修剪范式,即REAF,可以增强记忆保留,并使用非对称的遗忘.
- 通过防止无法恢复的信息丢失,克服当前过器修剪技术的局限性.
- 为了提高显瘦深度学习模型的性能上限.
主要方法:
- 介绍了记忆增强和基于的非对称遗忘 (REAF) 范式.
- 利用可融合的补偿卷曲来增强基线记忆,而无推断成本.
- 实施了基于过距离和内存增强的双边合作的修剪标准.
- 应用Ebbinghaus曲线为基础的非对称忘记在修剪过程中稳定学习.
主要成果:
- REAF显著优于现有的最先进的过器修剪方法 (SOTA).
- 在ImageNet上显示ResNet-50的FLOP (47.55%) 和参数 (42.98%) 显著减少.
- 在ImageNet上实现了最小的Top-1精度损失 (0.98%),展示了高效率和有效性.
- 拟议的方法使削减模型从基线限制中解放出来,并防止灾难性的遗忘.
结论:
- 通过平衡记忆增强和受控遗忘,REAF提供了一种优越的过削减方法.
- 该方法可以实现显著的模型压缩,同时保持高精度,这对于部署深度学习模型至关重要.
- REAF提供了一个强大而有效的战略,用于优化深度神经网络以提高效率.
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