Research on Cross - Domain Data Adaptation Technology in Transfer Learning

This paper focuses on the cross-domain data adaptation technology in transfer learning and conducts in-depth research. The existing relevant research results are comprehensively reviewed, covering various algorithms based on deep learning in recent years from traditional statistical methods. When analyzing the key methods, the cross-domain data adaptation technology based on neural networks is discussed in detail. With the powerful feature extraction ability of deep networks, this type of method has shown excellent performance in many fields such as image recognition and natural language processing. For example, in the image style transfer task, it can accurately capture the difference in image features between the source domain and the target domain, and achieve efficient style conversion; in the sentiment analysis task, it can effectively adapt to the language style differences of different text corpora and improve the accuracy of sentiment classification. However, it still faces many challenges, such as dependence on data labeling, loss function selection and component balance problems. In the future, we should focus on developing more general adaptation methods, exploring meta-learning-based technologies, and more effective ways to process cross-domain heterogeneous data, so as to promote more efficient application of transfer learning in different fields.