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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

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Citations:  Bibtex RefMan EndNote GB/T7714


Liang Hou


Jun Liang


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.7 P.1005-1018


Representation learning via a semi-supervised stacked distance autoencoder for image classification

Author(s):  Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang

Affiliation(s):  College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   jliang@zju.edu.cn

Key Words:  Autoencoder, Image classification, Semi-supervised learning, Neural network

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, 2020, 21(7): 1005-1018.

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A1 - Liang Hou
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DOI - 10.1631/FITEE.1900116

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.





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