CLC number: TP391.9
On-line Access: 2020-07-10
Received: 2019-02-28
Revision Accepted: 2019-09-16
Crosschecked: 2020-06-10
Cited: 0
Clicked: 5843
Citations: Bibtex RefMan EndNote GB/T7714
Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang. Representation learning via a semi-supervised stacked distance autoencoder for image classification[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.1900116 @article{title="Representation learning via a semi-supervised stacked distance autoencoder for image classification", %0 Journal Article TY - JOUR
半监督堆叠距离自动编码器的表征学习在图像分类上的应用浙江大学控制科学与工程学院,中国杭州市,310027 摘要:图像分类是深度学习的重要应用。在典型分类任务中,分类精度与通过深度学习方法提取的特征密切相关。自动编码器是一种特殊神经网络,常用于降维和特征提取。本文所提方法基于传统的自动编码器,将不同类别样本之间的"距离"信息纳入其中。该模型被称为半监督距离自动编码器。首先以无监督方式对每一层进行预训练。在随后的监督训练中,将优化的参数设置为初始值。为获得更好性能,使用堆叠式模型代替具有单一隐含层的传统自动编码器结构。开展一系列实验测试不同模型在几个数据集上的性能,包括MNIST数据集、街景门牌号码(SVHN)数据集、德国交通标志识别基准(GTSRB)和CIFAR-10数据集。将所提半监督距离自动编码器方法分别与传统自动编码器、稀疏自动编码器和监督自动编码器比较,实验结果证明该模型有效。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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