基于标签分布构造的异构数据集年龄估算
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作者单位:

1.福建船政交通职业学院信息与智慧交通学院,福州 350007;2.福州大学数学与计算机科学学院,福州 350108

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国家自然科学基金青年基金(62002063)资助项目。


Age Estimation for Isomerism Data Sets Based on Label Distribution Construction
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1.School of Information and Intelligent Transportation, Fujian Chuanzheng Communications College, Fuzhou 350007, China;2.Department of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China

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    摘要:

    现有年龄估算方法的性能度量主要是基于训练集与测试集独立同分布的假设。为了能更好地符合实际场景以及更好地评估年龄估算方法的泛化性能,提出一种异构数据集评估协议,即在年龄估算时更关注训练集与测试集具有的不同分布和特征情况。此外,为了提高基于卷积神经网络的年龄估算方法的拟合能力,在充分考虑相邻年龄特性的基础上,通过将年龄估算问题建模为基于高斯模型的标签分布学习,提出一种新颖的损失函数。理论分析与实验结果皆说明本文方法的有效性与鲁棒性。

    Abstract:

    Existing age estimation methods of performance measurement are mainly based on the training set and testing set of independent identically distributed hypothesis. In order to better conform to the actual scene and better assess the age estimation method of generalization performance, a kind of heterogeneous data sets to evaluate agreement is put forward, i.e. paying more attention to the training set and test set with different distribution and characteristics. In addition, in order to improve the age estimation method based on convolution neural network fitting ability, on the basis of fully considering the adjacent age characteristics, a new theory of loss function analysis is proposed through modeling the age estimation problem as the label distribution study based on Gauss model. Theoretical analysis and experimental results show the effectiveness and robustness of the proposed method.

    表 5 采用异构数据集评估协议在FG-NET中的性能对比(训练集:MORPH)Table 5 Performance comparison under proposed protocol and FG-NET (training set: MORPH)
    表 4 采用异构数据集评估协议在不同目标域中的性能对比(训练集:IMDB-WIKI-40k)Table 4 Performance comparison under proposed protocol and different testing sets (training set: IMDB-WIKI-40k)
    表 1 MORPH中采用随机分割协议的性能对比Table 1 Performance comparison under random splitting protocol in MPORH
    图1 KL与DC损失函数在不同高斯分布下的性能对比Fig.1 Performance comparison with KL and DC loss functions under two normal distributions
    图2 不同损失函数的比率Fig.2 Ratio of different loss functions
    图3 两种数据集层面的评估协议对比Fig.3 Comparison of different assessment protocols
    图4 不同超参数对MAE的影响Fig.4 Performance difference among different hyperparameters
    表 2 MORPH中采用S1/S2+S3和S2/S1+S3协议的性能对比Table 2 Performance comparison under S1/S2+S3 and S2/S1+S3 protocols in MPORH
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引用本文

王军祥,吴伶.基于标签分布构造的异构数据集年龄估算[J].数据采集与处理,2021,36(4):799-811

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  • 收稿日期:2020-12-20
  • 最后修改日期:2021-03-29
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  • 在线发布日期: 2021-07-25