弱监督分层深度学习的车辆识别算法

针对已有分类器在结构形式和训练方法的不足，构建了一个以二维深度置信网络（2D deep belief networks,2D DBN）为架构的弱监督分层深度学习车辆识别算法。首先，将传统一维的深度置信网络(Deep belief networks,DBN)扩展成2D-DBN，并构建相应分类器结构，从而能够直接以二维图像像素矩阵作为输入； 其次，在传统无监督训练的目标函数中，引入了一个具有适当权重的判别度正则化项，将原有无监督训练转化为带有较弱监督性的弱监督训练方式，从而使提取的特征较传统无监督特征更具判别性。多组对比实验表明，本文所提算法在识别率等指标上要优于已有深度学习算法。

Vehicle Recognition Algorithm Based on Weakly Supervised Hierarchical Deep Learning

Focusing on the shortage of structure and training methods of existing classifier, a weakly supervised hierarchical deep learning vehicle recognition algorithm with 2D deep belief networks(2D-DBN) is proposed. Firstly, the traditional one-dimensional deep belief network(DBN) is expanded to 2D-DBN, thus the pixel matrix of the 2-D images is taken as the input. Then, a determination regularization term with proper weight is introduced to the traditional unsupervised training objective function. By this change, the original unsupervised training is transferred to the weakly supervised training, so that the extracted features have more discrimination ability. Multiple sets of comparative experiments show that the proposed algorithm is better than other deep learning algorithms in respect of recognition rate.