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

arXiv 2211.09064 ↗First posted 14 Nov 2022cs.LG stat.APcited by 0

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Abstract (arXiv)

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).

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 56 references that are in the map. First posted on arXiv 14 Nov 2022.

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References · 6

  1. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
  2. 2014Deep Learning in Neural Networks: An Overviewcited by 765
  3. 2019Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labelingcited by 53
  4. 2019Transfer Adaptation Learning: A Decade Surveycited by 52
  5. 2017Joint Geometrical and Statistical Alignment for Visual Domain Adaptationcited by 43
  6. 2019Optimal Projection Guided Transfer Hashing for Image Retrievalcited by 8

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