Summary of Semi-supervised Transfer Boosting (ss-trboosting), by Lingfei Deng et al.
Semi-Supervised Transfer Boosting (SS-TrBoosting)
by Lingfei Deng, Changming Zhao, Zhenbang Du, Kun Xia, Dongrui Wu
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel fine-tuning framework for semi-supervised domain adaptation (SSDA), which trains high-performance models using few labeled target data, many unlabeled target data, and plenty of auxiliary data from a source domain. The proposed framework, called semi-supervised transfer boosting (SS-TrBoosting), consists of an initial well-trained model, boosted by additional base learners generated through supervised domain adaptation and semi-supervised learning. SS-TrBoosting is applicable to various existing SSDA approaches, leading to improved performance. Moreover, the authors extend their framework to semi-supervised source-free domain adaptation (SS-SFDA) for efficient data transmission and better data privacy protection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us train really good models that can work well in new situations with only a few examples. It’s like teaching a model to adapt to new environments, even if we don’t have many examples from those environments. The model learns by combining what it already knows with new information, making it better at guessing the correct answer. This is useful when we want to use models for things like image recognition or speech recognition in different places. |
Keywords
» Artificial intelligence » Boosting » Domain adaptation » Fine tuning » Semi supervised » Supervised
