| Autor: |
Liuyi Yang, Patrick Finnerty, Chikara Ohta |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Machine Learning with Applications, Vol 18, Iss , Pp 100601- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2666-8270 |
| DOI: |
10.1016/j.mlwa.2024.100601 |
| Popis: |
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 a new 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 a new 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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