Assessing the Value of Transfer Learning Metrics for Radio Frequency Domain Adaptation
| dc.contributor.author | Wong, Lauren J. | en |
| dc.contributor.author | Muller, Braeden P. | en |
| dc.contributor.author | McPherson, Sean | en |
| dc.contributor.author | Michaels, Alan J. | en |
| dc.date.accessioned | 2024-10-01T12:56:44Z | en |
| dc.date.available | 2024-10-01T12:56:44Z | en |
| dc.date.issued | 2024-07-25 | en |
| dc.date.updated | 2024-09-27T13:18:02Z | en |
| dc.description.abstract | The use of transfer learning (TL) techniques has become common practice in fields such as computer vision (CV) and natural language processing (NLP). Leveraging prior knowledge gained from data with different distributions, TL offers higher performance and reduced training time, but has yet to be fully utilized in applications of machine learning (ML) and deep learning (DL) techniques and applications related to wireless communications, a field loosely termed radio frequency machine learning (RFML). This work examines whether existing transferability metrics, used in other modalities, might be useful in the context of RFML. Results show that the two existing metrics tested, Log Expected Empirical Prediction (LEEP) and Logarithm of Maximum Evidence (LogME), correlate well with post-transfer accuracy and can therefore be used to select source models for radio frequency (RF) domain adaptation and to predict post-transfer accuracy. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Wong, L.J.; Muller, B.P.; McPherson, S.; Michaels, A.J. Assessing the Value of Transfer Learning Metrics for Radio Frequency Domain Adaptation. Mach. Learn. Knowl. Extr. 2024, 6, 1699-1719. | en |
| dc.identifier.doi | https://doi.org/10.3390/make6030084 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/121247 | en |
| dc.language.iso | en | en |
| dc.publisher | MDPI | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | machine learning | en |
| dc.subject | deep learning | en |
| dc.subject | transfer learning | en |
| dc.subject | domain adaptation | en |
| dc.subject | radio frequency machine learning | en |
| dc.title | Assessing the Value of Transfer Learning Metrics for Radio Frequency Domain Adaptation | en |
| dc.title.serial | Machine Learning and Knowledge Extraction | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |