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相关实验视频

Updated: Jul 5, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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深度监督的区块智能神经架构搜索

An Yang, Ying Liu, Chunguang Li

    IEEE transactions on neural networks and learning systems
    |January 17, 2024
    PubMed
    概括

    我们介绍了深度监督的区块智能神经架构搜索 (DBNAS),这是设计神经网络的资源友好的方法. DBNAS有效地搜索有前途的架构,显著降低计算成本和内存足迹.

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    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:

    • 神经架构搜索 (NAS) 自动化了神经网络设计.
    • 区块智能NAS解决了权重共享的局限性,但产生了高的计算成本.
    • 现有的方法使用监督蒸或对比学习,需要大量资源.

    研究的目的:

    • 提出一个资源友好的区块智能NAS方法.
    • 为了减轻当前区块智能的NAS技术的计算负担.
    • 为了实现高效和有效的神经网络架构优化.

    主要方法:

    • 开发了深度监督的区块智能NAS (DBNAS).
    • 在每个网络块之后集成轻量级深度监督模块.
    • 采用简单的监督学习方案,使用基础真理标签来逐步优化块.

    主要成果:

    • 在不到1个GPU日内,DBNAS在ImageNet上实现了高效的架构搜索.
    • 与现有的区块智能NAS方法相比,需要更少的GPU内存.
    • 最好的DBNAS模型在ImageNet上达到75.6%的Top-1精度,与最先进的模型相竞争.

    结论:

    • DBNAS为区块智能NAS提供了一个计算高效和有效的方法.
    • 该方法在ImageNet上表现出强的性能,并且可以很好地转移到其他数据集和任务.
    • DBNAS成功地将搜索效率与模型性能相平衡.

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