针对步态识别在反恐、安防领域亟待解决的小样本问题,提出了一种基于深度卷积神经网络(convolutional and neural network,CNN)和DLTL(dual learning and transfer learning)的步态虚拟样本生成方法。首先用基于VGG19的深度卷积神经网...针对步态识别在反恐、安防领域亟待解决的小样本问题,提出了一种基于深度卷积神经网络(convolutional and neural network,CNN)和DLTL(dual learning and transfer learning)的步态虚拟样本生成方法。首先用基于VGG19的深度卷积神经网络模型低层响应提取步态风格特征图,然后利用基于对抗网络的对偶学习(dual learning,DL)对风格特征图进行风格训练,得到风格特征模型;其次利用VGG19模型的高层响应提取步态内容特征图,然后让步态内容特征图对风格特征模型中的风格特征进行学习;最后使用迁移学习(transfer learning,TL)获得步态虚拟偏移样本。实验结果表明,经过DLTL风格学习生成的步态虚拟样本虽然整体风格发生改变,但人体步态特征没有改变,可有效扩充小样本容量;当虚拟样本增加到一定数量时,步态识别率有所提升。该方法与现有步态虚拟样本生成方法进行对比实验,结果表明该算法优于现有方法,能够大量生成虚拟样本且稳定提高步态识别的识别率。展开更多
Cross-project defect prediction(CPDP) uses one or more source projects to build a defect prediction model and applies the model to the target project. There is usually a big difference between the data distribution of...Cross-project defect prediction(CPDP) uses one or more source projects to build a defect prediction model and applies the model to the target project. There is usually a big difference between the data distribution of the source project and the target project, which makes it difficult to construct an effective defect prediction model. In order to alleviate the problem of negative migration between the source project and the target project in CPDP, this paper proposes an integrated transfer adaptive boosting(TrAdaBoost) algorithm based on multi-source data sets(MSITrA). The algorithm uses an existing two-stage data filtering algorithm to obtain source project data related to the target project from multiple source items, and then uses the integrated TrAdaBoost algorithm proposed in the paper to build a CPDP model. The experimental results of Promise’s 15 public data sets show that: 1) The cross-project software defect prediction model proposed in this paper has better performance in all tested CPDP methods;2) In the within-project software defect prediction(WPDP) experiment, the proposed CPDP method has achieved the better experimental results than the tested WPDP method.展开更多
文摘针对步态识别在反恐、安防领域亟待解决的小样本问题,提出了一种基于深度卷积神经网络(convolutional and neural network,CNN)和DLTL(dual learning and transfer learning)的步态虚拟样本生成方法。首先用基于VGG19的深度卷积神经网络模型低层响应提取步态风格特征图,然后利用基于对抗网络的对偶学习(dual learning,DL)对风格特征图进行风格训练,得到风格特征模型;其次利用VGG19模型的高层响应提取步态内容特征图,然后让步态内容特征图对风格特征模型中的风格特征进行学习;最后使用迁移学习(transfer learning,TL)获得步态虚拟偏移样本。实验结果表明,经过DLTL风格学习生成的步态虚拟样本虽然整体风格发生改变,但人体步态特征没有改变,可有效扩充小样本容量;当虚拟样本增加到一定数量时,步态识别率有所提升。该方法与现有步态虚拟样本生成方法进行对比实验,结果表明该算法优于现有方法,能够大量生成虚拟样本且稳定提高步态识别的识别率。
文摘Cross-project defect prediction(CPDP) uses one or more source projects to build a defect prediction model and applies the model to the target project. There is usually a big difference between the data distribution of the source project and the target project, which makes it difficult to construct an effective defect prediction model. In order to alleviate the problem of negative migration between the source project and the target project in CPDP, this paper proposes an integrated transfer adaptive boosting(TrAdaBoost) algorithm based on multi-source data sets(MSITrA). The algorithm uses an existing two-stage data filtering algorithm to obtain source project data related to the target project from multiple source items, and then uses the integrated TrAdaBoost algorithm proposed in the paper to build a CPDP model. The experimental results of Promise’s 15 public data sets show that: 1) The cross-project software defect prediction model proposed in this paper has better performance in all tested CPDP methods;2) In the within-project software defect prediction(WPDP) experiment, the proposed CPDP method has achieved the better experimental results than the tested WPDP method.
