Image classification is an important application of deep learning. In a typical classification task, the classification accuracy is strongly related to the features that are extracted via deep learning methods. An autoencoder is a special type of neural network, often used for dimensionality reduction and feature extraction. The proposed method is based on the traditional autoencoder, incorporating the “distance” information between samples from different categories. The model is called a semi-supervised distance autoencoder. Each layer is first pre-trained in an unsupervised manner. In the subsequent supervised training, the optimized parameters are set as the initial values. To obtain more suitable features, we use a stacked model to replace the basic autoencoder structure with a single hidden layer. A series of experiments are carried out to test the performance of different models on several datasets, including the MNIST dataset, street view house numbers (SVHN) dataset, German traffic sign recognition benchmark (GTSRB), and CIFAR-10 dataset. The proposed semi-supervised distance autoencoder method is compared with the traditional autoencoder, sparse autoencoder, and supervised autoencoder. Experimental results verify the effectiveness of the proposed model.

图像分类是深度学习的重要应用。在典型的分类任务中，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的神经网络，通常用于降维和特征提取。所提出的方法基于传统的自动编码器，并结合了来自不同类别的样本之间的“距离”信息。该模型称为半监督距离自动编码器。首先以无监督的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。为了获得更合适的功能，我们使用了一个堆叠模型，用一个隐藏层替换了基本的自动编码器结构。进行了一系列实验来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。将提出的半监督距离自动编码器方法与传统的自动编码器，稀疏自动编码器和监督自动编码器进行了比较。实验结果验证了该模型的有效性。