Image Classification Based on Mixed Deep Learning Model Transfer Learning

Abstract

Abstract: In order to obtain high discrimination image representations in limited amount of datasets, the method based on mixed deep transfer learning model was proposed. When trained CNNs transferred to the target datasets, fully-connected layers were replaced by RBM layers. The method retrained the RBM layers and Softmax classifier, then fine-tuned the mixed model with backpropagation algorithm. The RBM layers not only fully connected whole feature maps, but also learned the target datasets' statistical features in the view of the biggest logarithmic likelihood, to eliminate the effects caused by the content differences between datasets. The experimental results show that the method has improved the accuracy of image classification, outperforming other methods on Pascal VOC2007 and Caltech101 datasets.

Key words: image classification, CNN(Convolutional Neural Networks), RBM(Restricted Boltzmann Machines), transfer learning, softmax

CLC Number:

TP391.9

Shi Xiangbin, Fang Xuejian, Zhang Deyuan, Guo Zhongqiang. Image Classification Based on Mixed Deep Learning Model Transfer Learning[J]. Journal of System Simulation, 2016, 28(1): 167-173.

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