Melting Reduction Auto-Encoder

Abstract

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.

Keywords

• auto-encoder (ae)
• feature extraction
• melting reduction auto-encoder (mrae)
• ablation network structure
• loss function of joint reconstruction