方法对比
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| 正则化迁移学习× | 少样本学习× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2000s–2010s | 2011–2017 |
| 提出者≠ | Pan, S. J. & Yang, Q. (survey); regularization variants by multiple authors | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| 类型≠ | Regularized supervised/semi-supervised learning framework | Meta-learning / low-data learning paradigm |
| 开创性文献≠ | Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ | Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29. link ↗ |
| 别名 | regularized domain adaptation, transfer learning with regularization, penalized transfer learning, regularized fine-tuning | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| 相关≠ | 6 | 4 |
| 摘要≠ | Regularized Transfer Learning applies explicit penalty terms to a transfer learning pipeline to control how much a model shifts away from source-domain knowledge when adapting to a new target domain. The regularizer discourages negative transfer — the harmful carry-over of irrelevant source patterns — while preserving beneficial shared representations and preventing overfitting when target-domain labels are scarce. | Few-shot learning is a machine learning paradigm that trains models to recognize new classes or solve new tasks from only a handful of labeled examples — typically one to five — by leveraging prior knowledge acquired from a large, related training distribution. It is especially relevant in domains where labeling is expensive, scarce, or structurally limited. |
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