Applications of cluster-based transfer learning in image and localization tasks

被引:0
|
作者
Yang, Liuyi [1 ]
Finnerty, Patrick [1 ]
Ohta, Chikara [1 ]
机构
[1] Kobe Univ, Grad Sch Syst Informat, Kobe 6578501, Japan
来源
MACHINE LEARNING WITH APPLICATIONS | 2024年 / 18卷
关键词
Semi-supervised transfer learning; Fine-tune; Localization; Image recognition; DOMAIN ADAPTATION;
D O I
10.1016/j.mlwa.2024.100601
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Transfer learning can address the issue of insufficient labels in machine learning. Using knowledge in a labeled domain (source domain) can assist in acquiring and learning knowledge in a domain (target domain) that lacks some or all labels. In this paper, we propose anew cluster-based semi-supervised transfer learning (CBSSTL) under a new assumption that samples in the target domain are unlabeled but contain cluster information. Furthermore, we propose anew transfer learning framework and a method for fine-tuning parameters. We tested and compared the proposed method with other unsupervised and semi-supervised transfer learning methods on well-known image datasets. The experimental results demonstrate the effectiveness of the proposed method. Additionally, we created a localization dataset for transfer learning. Finally, we tested and analyzed the proposed method on this dataset. Its particularly challenging nature makes it difficult for our method to work effectively.
引用
收藏
页数:13
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