系统地调查稀疏扰乱的敏度意识最小化优化器
概括
稀疏SAM (SSAM) 通过将稀疏扰动应用于深度神经网络训练来降低计算开销. 这种高效的方法与50%稀疏度的度意识最小化 (SAM) 相比,保持或提高了性能.
科学领域:
- 机器学习 机器学习
- 深度学习优化优化
背景情况:
- 深度神经网络 (DNN) 由于复杂的损失景观,难以进行概括.
- 敏度意识最小化 (SAM) 可以平滑损失场景,但由于干扰所有权重而导致高计算成本.
研究的目的:
- 引入Sparse SAM (SSAM),一个高效的培训方案,可以减少SAM的计算开销.
- 研究稀疏扰动策略,以提高DNN训练效率和概括性.
主要方法:
- 对于稀疏重量扰动,SSAM采用了一个二进制面具.
- 使用费舍尔信息和动态稀疏训练,衍生了稀疏面具.
- 调查了各种面具模式 (非结构化,结构化,N:M) 和扰动实现 (明确的,隐含的).
主要成果:
- SSAM实现了与SAM相当的收率 ($O(\log T/\sqrt{T}) $).
- 在CIFAR和ImageNet-1K上的实验表明SSAM的效率优于SAM.
- 在高达50%的稀疏度下,性能保持或增强.
结论:
- 在DNN培训中,SSAM为SAM提供了一种有效且计算效率高的替代方案.
- 稀疏扰动是优化DNN而不牺牲性能的一种可行的策略.
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