Assessing the Value of Transfer Learning Metrics for Radio Frequency Domain Adaptation

dc.contributor.authorWong, Lauren J.en
dc.contributor.authorMuller, Braeden P.en
dc.contributor.authorMcPherson, Seanen
dc.contributor.authorMichaels, Alan J.en
dc.date.accessioned2024-10-01T12:56:44Zen
dc.date.available2024-10-01T12:56:44Zen
dc.date.issued2024-07-25en
dc.date.updated2024-09-27T13:18:02Zen
dc.description.abstractThe 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.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationWong, 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.doihttps://doi.org/10.3390/make6030084en
dc.identifier.urihttps://hdl.handle.net/10919/121247en
dc.language.isoenen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleAssessing the Value of Transfer Learning Metrics for Radio Frequency Domain Adaptationen
dc.title.serialMachine Learning and Knowledge Extractionen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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