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一个渐变导向的进化神经架构搜索

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    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:

    • 神经架构搜索 (NAS) 自动化了深度神经网络设计,但在计算上是密集的.
    • 现有的NAS方法在效率和性能优化方面存在局限性.

    研究的目的:

    • 提出一种新的混合算法,渐变引导进化NAS (GENAS),用于高效的卷积神经网络 (CNN) 设计.
    • 解决传统NAS和梯度下降方法的计算成本和局限性.

    主要方法:

    • GENAS将进化的全球和本地搜索运营商结合在从超级网络中抽取样本的子网络群体上.
    • 候选架构被编码在一个表中,使用新型交叉和突变运算符进行操作.
    • 在未经再培训的情况下,对候选架构应用灵感来自可差异化的NAS的本地搜索.

    主要成果:

    • 在GENAS的测试中,测试误差很低:2.45% (CIFAR-10),16.86% (CIFAR-100) 和23.9% (ImageNet).
    • 该方法表现出显著的计算效率,只需要0.26 GPU天.
    • 分离的子网评估阻止了超级网络内部的强有力的操作合.

    结论:

    • GENAS有效地加快了NAS的培训和评估过程.
    • 提出的方法成功地获得了用于图像分类的高性能CNN结构.
    • GENAS克服了纯粹进化或梯度下降NAS方法的局限性.