[1]WANG Dewen,WEI Botao.A small-sample image classification method based on a Siamese variational auto-encoder[J].CAAI Transactions on Intelligent Systems,2021,16(2):254-262.[doi:10.11992/tis.201906022]
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A small-sample image classification method based on a Siamese variational auto-encoder

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 2 Page number: 254-262 Column: 学术论文—机器感知与模式识别 Public date: 2021-03-05
Title:
A small-sample image classification method based on a Siamese variational auto-encoder
Author(s):
WANG Dewen;  WEI Botao
School of Control and Computer Engineering, North China Electric Power University, Baoding 071000, China
Keywords:
small sample; variational auto-encoder (VAE); siamese network; image recognition; over-fitting; eigenvector; deep learning; data augmentation
CLC:
TP183
DOI:
10.11992/tis.201906022
Abstract:
Currently, most deep learning is based on the use of large amounts of data and the construction of a deep network to achieve automatic recognition, but it is difficult to obtain a large amount of sample data in many scenarios. To solve this problem, we propose the use of a small-sample image classification method based on a Siamese variational auto-encoder (S-VAE). First, the high-level semantic features of the original training data are extracted by the variational auto-encoder. Then, the input structure of the Siamese network is constructed by the encoders of two trained variational auto-encoders. Lastly, the samples are identified by the classifier. The variational auto-encoder can solve the over-fitting problem caused by small amounts of sample data, and the structure of the Siamese network increases the training number when the sample size is small. The experimental results on the Omniglot dataset show that this method has a 3.1% average improvement in accuracy compared with the original Siamese network. The model convergence speed is also faster, which proves that the S-VAE can better complete the classification task when using small-sample data.
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