Research on fault diagnosis model driven by artificial intelligence from domain adaptation to domain generalization

Abstract

Transfer learning is studied through inductive research, knowledge transfer, lifelong learning and cumulative learning. The research scope and field of transfer learning are very wide. Transfer learning is a training method that can transfer the network structure and weight originally used to solve the mature task methodology to the new learning task, and can also get better results in the new task. The reason why transfer learning can be realized is that the characteristics of convolutional neural network in shallow learning are universal. In the case of insufficient samples, transfer learning can be used to transfer these general feature learning from other trained networks, so as to save training time and obtain better recognition results.