关键词: 迁移学习, 开集域适应, 数据增强, 特征提取, 分类器

Abstract: Domain adaptation, as a critical technique in transfer learning, effectively addresses the issue of differing distributions between training and testing datasets. However, traditional domain adaptation methods were typically limited to scenarios where the number and types of categories in the target and source domain datasets were identical. In practical applications, this condition was often difficult to meet. Open - set Domain Adaptation (OSDA) emerged to address this challenge. In order to fill the gap in this field and provide a reference for related research, a summary and analysis of the open - set domain adaptation methods that had emerged in recent years were conducted. The related concepts and basic structures were introduced. The typical methods were combed and analyzed from three stages: data augmentation, feature extraction, and classifier. Future development directions were also looked forward to.

Key words: transfer learning, open set domain adaptation, data augmentation, feature extraction, classifier