Integrated Transfer Learning Method for Image Recognition Based on Neural Network
Jingyuan He, Bailong Yang, Yang Su · IETE Journal of Research · 2021
As recognition technology advances in various fields, the problem of requiring a large number of correct image recognition is also accompanied. So as to promote the correctness of image recognition, we adopted two Convolutional Neural Network (CNN) integrated migration learning network models VGG-16 CNN and LeNet-5 CNN. Experimental results prove that the performance of image recognition in various models are improved. Also, the migration learning and convolutional neural network are used to construct a K-selection migration learning algorithm and a migration framework model whose parameters are migrated to the image data set. The use of a label data set with a large sample size, the selection of a pre-trained convolutional neural network model with a deep layer of complex network structure and the selection of an appropriate network fine-tuning depth can optimize the migration adaptation effect of the migration network. The image recognition precision of the algorithm is improved by about 3%, which further improves the migration adaptation effect.