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OStr-DARTS:基于操作强度的可差分的神经架构搜索
IEEE transactions on cybernetics
|September 27, 2024
概括
微分架构搜索 (DARTS) 可能导致神经网络架构不佳. 本研究提出了一种基于操作强度的新操作选择方法,有效地解决了DARTS退化问题,而不改变超级网络优化.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 微分架构搜索 (DARTS) 是神经架构搜索 (NAS) 的一个高效方法.
- DARTS优化了混合操作的超级网络,并根据操作贡献选择了最佳架构.
- 在DARTS中的一个关键挑战是退化问题,导致次优架构.
研究的目的:
- 为了调查DARTS退化问题的原因.
- 提出一种新的操作选择标准,以减轻退化问题.
- 证明拟议方法在提高DARTS稳定性的有效性.
主要方法:
- 提出了基于运营强度的新选择标准,通过对最终损失的影响来估计其重要性.
- 用提出的标准取代传统的基于大小的选择方法.
- 在没有修改超级网络优化的情况下,对NAS-Bench-201和DARTS搜索空间的方法进行了验证.
主要成果:
- 拟议的操作强度标准有效地解决了DARTS退化问题.
- 该方法可以提高DARTS的稳定性,而不会改变超级网络优化过程.
- 在NAS-Bench-201和DARTS搜索空间上的实验结果证实了该方法的有效性.
结论:
- 基于大小的选择方法是导致DARTS不稳定性的关键因素.
- 拟议的操作强度标准为DARTS退化问题提供了可行的解决方案.
- 这项研究提供了一种更强大的方法,用于使用DARTS搜索神经架构.
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