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使用增强学习训练多焦点聚焦模型

刘畅 刘勤让

刘畅, 刘勤让. 使用增强学习训练多焦点聚焦模型. 自动化学报, 2017, 43(9): 1563-1570. doi: 10.16383/j.aas.2017.c160643
引用本文: 刘畅, 刘勤让. 使用增强学习训练多焦点聚焦模型. 自动化学报, 2017, 43(9): 1563-1570. doi: 10.16383/j.aas.2017.c160643
LIU Chang, LIU Qin-Rang. Using Reinforce Learning to Train Multi-attention Model. ACTA AUTOMATICA SINICA, 2017, 43(9): 1563-1570. doi: 10.16383/j.aas.2017.c160643
Citation: LIU Chang, LIU Qin-Rang. Using Reinforce Learning to Train Multi-attention Model. ACTA AUTOMATICA SINICA, 2017, 43(9): 1563-1570. doi: 10.16383/j.aas.2017.c160643

使用增强学习训练多焦点聚焦模型

doi: 10.16383/j.aas.2017.c160643
基金项目: 

国家高技术研究发展计划(863计划) 2014AA01A

国家自然科学基金 61572520

详细信息
    作者简介:

    刘勤让 国家数字交换系统工程技术研究中心研究员.主要研究方向为片上网络设计. E-mail: qinrangliu@sina.com

    通讯作者:

    刘畅 国家数字交换系统工程技术研究中心硕士研究生.主要研究方向为人工智能和芯片技术.本文通信作者.E-mail: liunux1992@gmail.com

Using Reinforce Learning to Train Multi-attention Model

Funds: 

National High Technology Research and Development Program of China (863 Program) 2014AA01A

National Natural Science Foundation of China 61572520

More Information
    Author Bio:

    Researcher at China National Digital Switching System Engineering and Technological Research and Development Center. His main research interest is network-on-chip

    Corresponding author: LIU Chang Master student at China National Digital Switching System Engineering and Technological Research and Development Center. His research interest covers artificial intelligence and chip design technology. Corresponding author of this paper
  • 摘要: 聚焦模型(Attention model,AM)将计算资源集中于输入数据特定区域,相比卷积神经网络,AM具有参数少、计算量独立输入和高噪声下正确率较高等优点.相对于输入图像和识别目标,聚焦区域通常较小;如果聚焦区域过小,就会导致过多的迭代次数,降低了效率,也难以在同一输入中寻找多个目标.因此本文提出多焦点聚焦模型,同时对多处并行聚焦.使用增强学习(Reinforce learning,RL)进行训练,将所有焦点的行为统一评分训练.与单焦点聚焦模型相比,训练速度和识别速度提高了25%.同时本模型具有较高的通用性.
    1)  本文责任编委 袁勇
  • 图  1  单焦点聚焦模型识别过程

    Fig.  1  Recognition process of RAM

    图  2  模型结构

    Fig.  2  Model structure

    图  3  多焦点模型识别过程

    Fig.  3  Recognition process of multi attention model

    图  4  多焦点模型在60像素× 60像素图像中识别的效果

    Fig.  4  Recognition process of multi attention model in 60 × 60 image dataset

    图  5  多焦点聚焦模型相比RAM的对照试验

    Fig.  5  Control experiment between RAM and multi attention model

    图  6  对比训练时收敛速度

    Fig.  6  Convergence speed control experiment

    图  7  焦点数量与正确率关系

    Fig.  7  Relationship between quantify and accuracy

    图  8  焦点数量与运行速度关系

    Fig.  8  Relationship between quantify and speed

    表  1  多焦点模型错误率

    Table  1  Multi-attention model error rate

    模型 错误率(%)
    RAM, 2次 8.11
    RAM, 4次 3.28
    RAM, 6次 2.11
    RAM, 8次 1.55
    RAM, 10次 1.26
    多焦点模型, 2次 4.17
    多焦点模型, 4次 2.59
    多焦点模型, 6次 1.58
    多焦点模型, 8次 1.19
    多焦点模型, 10次 1.19
    下载: 导出CSV

    表  2  随机坐标错误率

    Table  2  Random position error rate

    模型 错误率(%)
    RAM, 2次 1.51
    RAM, 4次 1.29
    RAM, 6次 1.22
    多焦点模型, 2次 2.81
    多焦点模型, 4次 1.55
    多焦点模型, 6次 1.01
    下载: 导出CSV

    表  3  噪声环境对比

    Table  3  Noisy dataset error rate between RAM and multi attention model

    模型 错误率(%)
    RAM, 2次 4.96
    RAM, 4次 4.08
    RAM, 6次 4.04
    多焦点模型, 2次 5.25
    多焦点模型, 4次 4.47
    多焦点模型, 6次 3.43
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-09-08
  • 录用日期:  2017-03-21
  • 刊出日期:  2017-09-20

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