Author: Zen and the Art of Computer Programming
1 Introduction
With the continuous improvement of deep learning models and the continuous expansion of data sets, traditional machine learning tasks are becoming more and more difficult to solve, especially on some complex and rare data sets. The transfer learning technology of deep learning models learns model parameters with relatively good generalization performance by using experience in other fields or tasks, which greatly promotes the development of deep learning. This paper first introduces the concept, mechanism and advantages of transfer learning, and then combines the transfer learning framework Tensorflow-Slim to realize the transfer learning method of adaptive network, and the experiment on the MNIST data set proves that this method effectively improves the classification accuracy. Next, we will explain the practical application of transfer learning in vision, natural language processing and other fields, and share its specific effects in different scenarios. Finally, we will point out the challenges of transfer learning, look forward to its future research directions, and give expectations for the progress of this field.
2. Overview of transfer learning
2.1 Introduction to transfer learning
Transfer learning is an important research topic in the field of deep learning, which enables a deep neural network to learn the knowledge of another deep neural network, so as to achieve better or even better performance than the original network performance. Transfer learning mainly includes two types of strategies:
- Feature Extraction: Use the pre-trained model as a fixed feature extractor to fine-tune the model parameters on the target dataset to obtain a new model. By sharing the underlying feature extractor, transfer learning can better improve the learning efficiency of the target task and reduce the training time.
- Retraining: directly retrain the entire model, and jointly train the parameters of the two models based on the source data and the target data. By using only a small amount of labeled source data, transfer learning can take advantage of the bias of the source data for training, and achieve better results than training the target data set alone.
2.2 The key to transfer learning
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