Renewing Iterative Self-labeling Domain Adaptation with Application to the Spine Motion Prediction

Renewing Iterative Self-labeling Domain Adaptation with Application to the Spine Motion Prediction

The area of transfer learning comprises supervised machine learning methods that cope with the issue when the training and testing data have different input feature spaces or distributions. In this work, we propose a novel transfer learning algorithm called Renewing Iterative Self-labeling Domain Ad...

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Veröffentlicht in:arXiv.org 2022-11
Hauptverfasser: Chen, Gecheng, Zhou, Yu, Zhang, Xudong, Tuo, Rui
Format: Artikel
Sprache:eng
Schlagworte:
Adaptation
Algorithms
Domains
Labeling
Machine learning
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Zusammenfassung:The area of transfer learning comprises supervised machine learning methods that cope with the issue when the training and testing data have different input feature spaces or distributions. In this work, we propose a novel transfer learning algorithm called Renewing Iterative Self-labeling Domain Adaptation (Re-ISDA). In this work, we propose a novel transfer learning algorithm called Renewing Iterative Self-labeling Domain Adaptation (Re-ISDA).
ISSN:2331-8422