Review of Image Classification Method Based on Deep Transfer Learning

With the continuous development of deep learning technology, neural networks such as convolutional neural network (CNN) have shown good performance in many fields, such as image processing. Meanwhile, the relevant algorithm has made great progress. But the experiment results show that the deeper the network layers, the more the number of parameters that need to be trained in neural network, and the massive computing resources will be consumed to reconstruct and train the deep convolutional neural network (DCNN) model. These parameters often need to be trained in large dataset. But in many practical applications, the effective sample dataset that can be collected are usually small and lack of annotated samples. It is a pity that models that perform well on large datasets often have overfitting problems when applied to small datasets. And transfer learning can recognize and apply knowledge and skills learned in previous domains/tasks to novel domains/tasks. Combining deep convolutional neural network learning with transfer learning can make full use of existing models with good performance to solve problems in new fields, so has received considerable attentions due to its high research value and wide application prospect. This paper focuses on the combination of CNN and transfer learning, analyzes their characteristics, summarizes the relevant models, methods and applications, so as to promote their effective fusion in image classification.