Journal of Frontiers of Computer Science and Technology ›› 2021, Vol. 15 ›› Issue (8): 1526-1533.DOI: 10.3778/j.issn.1673-9418.2008046

• Artificial Intelligence • Previous Articles     Next Articles

Melting Reduction Auto-Encoder

SUN Yu, WEI Benzheng, LIU Chuan, ZHANG Kuixing, CONG Jinyu   

  1. 1. College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
    2. Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266122, China
    3. Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266122, China
  • Online:2021-08-01 Published:2021-08-02



  1. 1. 山东中医药大学 智能与信息工程学院,济南 250355
    2. 山东中医药大学 医学人工智能研究中心,山东 青岛 266122
    3. 山东中医药大学 青岛中医药科学院,山东 青岛 266122


Auto-encoder (AE) is one of the simple and widely used unsupervised feature extraction algorithms of deep learning. Existing automatic encoders for image feature extraction remain some problems such as insufficient feature extraction and excessive model parameters, etc. Aiming at above problems, MRAE (melting reduction auto-encoder) is proposed for image feature extraction in this paper. Firstly, an “ablation network structure” is proposed in the algorithm. It can realize feature enrichment through feature cross fusion in the encoder and reduce feature loss and parameters of model by optimizing the decoding structure in the decoder. Secondly, a joint reconstruction loss function is designed. It calculates the reconstruction loss between feature layers to increase the relationship between feature layers and avoid the prematurity of the model. The experimental results show that the accuracy of the feature extracted by MRAE using different classifiers, such as SVM (support vector machine), K-means, and CART (classification and regression tree), is more than 97% on lung CT image datasets. The accuracy of the feature extracted by MRAE using fully connection is more than 90% on the CvD (cats vs. dogs) dataset.

Key words: auto-encoder (AE), feature extraction, melting reduction auto-encoder (MRAE), ablation network structure, loss function of joint reconstruction



关键词: 自动编码器(AE), 特征提取, 融减自动编码器(MRAE), 融减网络结构, 联合重构损失函数