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摘要: 深度监督学习在合成孔径雷达自动目标识别任务中的成功依赖于大量标签样本。但是，在大规模数据集中经常存在错误(噪声)标签，很大程度降低网络训练效果。该文提出一种基于损失曲线拟合的标签噪声不确定性建模和基于噪声不确定度的纠正方法：以损失曲线作为判别特征，应用无监督模糊聚类算法获得聚类中心和类别隶属度以建模各样本标签噪声不确定度；根据样本标签噪声不确定度将样本集划分为噪声标签样本集、正确标签样本集和模糊标签样本集，以加权训练损失方法分组处理训练集，指导分类网络训练实现纠正噪声标签。在MSTAR数据集上的实验证明，该文所提方法可处理数据集中混有不同比例标签噪声情况下的网络训练问题，有效纠正标签噪声。当训练数据集中标签噪声比例较小(40%)时，该文所提方法可纠正98.6%的标签噪声，并训练网络达到98.7%的分类精度。即使标签噪声比例很大(80%)时，该文方法仍可纠正87.8%的标签噪声，并训练网络达到82.3%的分类精度。
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关键词:
- 合成孔径雷达 /
- 标签噪声 /
- 标签噪声纠正 /
- 标签噪声不确定性建模 /
- 模糊聚类算法
Abstract: The success of deep supervised learning in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) relies on a large number of labeled samples. However, label noise often exists in large-scale datasets, which highly influence network training. This study proposes loss curve fitting-based label noise uncertainty modeling and a noise uncertainty-based correction method. The loss curve is a discriminative feature to model label noise uncertainty using an unsupervised fuzzy clustering algorithm. Then, according to this uncertainty, the sample set is divided into different subsets: the noisy-label set, clean-label set, and fuzzy-label set, which are further used in training loss with different weights to correct label noise. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset prove that our method can deal with varying ratios of label noise during network training and correct label noise effectively. When the training dataset contains a small ratio of label noise (40%), the proposed method corrects 98.6% of these labels and trains the network with 98.7% classification accuracy. Even when the proportion of label noise is large (80%), the proposed method corrects 87.8% of label noise and trains the network with 82.3% classification accuracy. -
图 1 混合正确和噪声标签样本训练分类网络的损失曲线(以含80%标签噪声数据集训练ResNet18网络300次获得)
Figure 1. Loss curves of the classification network trained by mixed-noisy-and-clean labels (obtained by training ResNet18 with 80% noisy labels after 300 epochs)
图 2 混合正确和噪声标签样本训练分类网络的损失曲线(以含80%标签噪声数据集训练ResNet18网络300代获得)和聚类中心
Figure 2. Loss curves of the classification network trained by mixed-noisy-and-clean labels (obtained by training ResNet18 with 80% noisy labels after 300 epochs) and clustering centers for clean and noisy labels
图 4 本文方法训练分类网络的损失曲线
Figure 4. Loss curves of clean and noisy samples for our proposed method
表 1 标签噪声数据集训练分类网络模型精度(%)
Table 1. Classification accuracy of the models trained with noisy labels and clean labels (%)
训练网络模型 分类精度 20%的正确标签 92.13 混合20%的正确标签 & 80%的噪声标签 27.46 100%的正确标签 98.30 
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1 含噪标签数据集训练分类网络
1. Train a classification network with noisy labels
输入：X：训练集中的图像样本。 ${Y_{\mathrm{n}}}$：训练集中的样本标签(包括噪声标签)。 $ f\left( { \cdot ;\theta } \right) $：一个分类网络(本文中为CNN)。 输出：CNN训练权重$ {\theta ^{{T_2}}} $ 步骤1：混有噪声和正确标签数据集$(X,{Y_{\mathrm{n}}})$训练网络${T_1}$次 获得$ f\left( { \cdot ;{\theta ^{{T_1}}}} \right) $和$\left\{ {{{\boldsymbol{l}}}_i^{{T_1}}} \right\}_{i = 1}^N$。 步骤2：标签噪声不确定性建模 初始化c； for i in $ \left[1,T_c\right] $ iterations: do 根据式(2)计算${\boldsymbol{c}}(k)$ 根据式(3)计算${\mu _i}(k)$ end for 获得${{\boldsymbol{c}}^ * }(k)$和$ \mu _i^ * (k) $ 步骤3：基于噪声不确定度的数据划分 获得噪声样本集$ {D_{\mathrm{n}}} $、正确样本集$ {D_{\mathrm{c}}} $、模糊样本集$ {D_{\mathrm{f}}} $ $ {D_{\text{n}}} = \left\{ {({{\boldsymbol{x}}_i},{{\boldsymbol{y}}_i})|\mu _i^ * (2) > {\tau _1}} \right\}_{i = 1}^N $ $ {D_{\mathrm{c}}} = \left\{ {({{\boldsymbol{x}}_i},{{\boldsymbol{y}}_i})|\mu _i^ * (2) < {\tau _2}} \right\}_{i = 1}^N $ $ {D_{\mathrm{f}}} = \left\{ {({{\boldsymbol{x}}_i},{{\boldsymbol{y}}_i})|{\tau _2} \le \mu _i^ * (2) \le {\tau _1}} \right\}_{i = 1}^N $ 通过mixup数据增强获得$ {D_{{\text{aug}}}} $ 根据式(5)计算${{\boldsymbol{x}}_{{\mathrm{mix}}}}$ 根据式(6)计算${{\boldsymbol{y}}_{{\mathrm{mix}}}}$ $ {D_{{\text{aug}}}} = \left\{ {\left( {{{\boldsymbol{x}}_{{\text{mix}}}},{{\boldsymbol{y}}_{{\text{mix}}}}} \right)} \right\} $ $ {D_{\mathrm{m}}} = {D_{\text{c}}} \cup {D_{\mathrm{f}}} \cup {D_{{\mathrm{aug}}}} $ 步骤4：使用${D_{\mathrm{m}}}$分组加权纠正训练网络${T_2}$次
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表 2 MSTAR数据集中训练和测试数据集中的目标数量
Table 2. Number of targets in the training and testing datasets of the MSTAR dataset
地面目标图像 训练数据集 测试数据集 总数据集 2S1 299 274 573 BMP2 233 195 428 BRDM2 298 274 572 BTR60 256 195 451 BTR70 233 196 429 D7 299 274 573 T62 299 273 572 T72 232 196 428 ZIL131 299 274 573 ZSU234 299 274 573
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表 3 不同比例标签噪声下的纠正精度(%)
Table 3. The correction accuracy with different noise ratio (%)
算法 40 (36.4) 60 (54.0) 80 (72.5) BMM-based 98.7 84.8 61.1 LNMC 97.9 92.2 78.1 本文方法 98.6 97.2 87.8 注：由于制作标签噪声时一些样本标签可能被随机重新标注为正确标签，实际噪声比例略低于设置比例，括号内为实验中真实标签噪声比例。加粗项表示最优结果。
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表 4 不同比例标签噪声下的网络分类精度(%)
Table 4. The classification accuracy with different noise ratio (%)
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表 5 噪声比80%下$ {{\boldsymbol{\tau}} _1},{\boldsymbol{{\tau}} _2} $不同取值时的网络分类精度
Table 5. The classification accuracy with different $ {{\boldsymbol{\tau}} _1},{{\boldsymbol{\tau}} _2} $ values under 80% noise ratio
$ {\tau _1} $ $ {\tau _2} $ 分类精度(%) 0.7 0.1 80.8 0.8 0.1 80.9 0.9 0.1 80.9 0.7 0.2 82.7 0.8 0.2 82.3 0.9 0.2 82.5 0.7 0.3 82.2 0.8 0.3 82.3 0.9 0.3 82.3
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表 6 噪声比40%下$ {{\boldsymbol{\tau}} _1},{{\boldsymbol{\tau}} _2} $不同取值时的网络分类精度
Table 6. The classification accuracy with different $ {{\boldsymbol{\tau}} _1},{{\boldsymbol{\tau}} _2} $ values under 40% noise ratio
$ {\tau _1} $ $ {\tau _2} $ 分类精度(%) 0.7 0.1 96.4 0.8 0.1 96.8 0.9 0.1 96.6 0.7 0.2 97.6 0.8 0.2 98.7 0.9 0.2 97.5 0.7 0.3 97.6 0.8 0.3 97.6 0.9 0.3 97.4
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表 7 Mixup方法消融实验(%)
Table 7. Ablation study on mixup method (%)
是否使用mixup 40 (36.4) 60 (54.0) 80 (72.5) 否 94.7 90.3 73.2 是 98.7 94.9 82.3 注：由于制作标签噪声时一些样本标签可能被随机重新标注为正确标签，实际噪声比例略低于设置比例，括号内为实验中真实标签噪声比例。
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