带噪声标签数据的滤波加权校正训练方法

Yulong Wang, Xiaohui Hu, Zheshu Jia
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引用次数: 0

摘要

针对噪声数据集下模型精度低的问题,提出了一种滤波加权校正训练方法。该方法采用模型微调的思想,利用过滤后的数据对训练好的深度神经网络模型进行调整和校正,具有较高的可移植性。在数据滤波过程中,基于双区间随机阈值的噪声标签滤波算法减少了对人为设置参数的依赖,提高了随机阈值的可靠性,提高了滤波精度和干净样本的召回率。在标定过程中,为了解决样本不平衡的问题,对不同类型的样本进行加权,提高模型的有效性。实验结果表明,该方法可以提高深度神经网络模型的F1值。
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Filtered Weighted Correction Training Method for Data with Noise Label
To solve the problem of low model accuracy under noisy data sets, a filtered weighted correction training method is proposed. This method uses the idea of model fine-tuning to adjust and correct the trained deep neural network model using filtered data, which has high portability. In the data filtering process, the noise label filtering algorithm, which is based on the random threshold in the double interval, reduces the dependence on artificially set parameters, increases the reliability of the random threshold, and improves the filtering accuracy and the recall rate of clean samples. In the calibration process, to deal with sample imbalance, different types of samples are weighted to improve the effectiveness of the model. Experimental results show that the propose method can improve the F1 value of deep neural network model.
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