方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 迁移学习× | 少样本学习× | 自监督学习× | |
|---|---|---|---|
| 领域 | 机器学习 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning | Machine learning |
| 起源年份≠ | 2010 (formalized); 1990s (early roots) | 2011–2017 | 2018–2020 |
| 提出者≠ | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) | Lake, B. M.; Vinyals, O.; Finn, C. et al. | LeCun, Y. and community (formalized ~2018–2020) |
| 类型≠ | Learning paradigm | Meta-learning / low-data learning paradigm | Representation 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 ↗ | LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/ link ↗ |
| 别名 | TL, domain adaptation, fine-tuning, pre-trained model adaptation | FSL, low-shot learning, k-shot learning, meta-learning for few examples | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning |
| 相关≠ | 3 | 4 | 3 |
| 摘要≠ | Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond. | 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. | Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples. |
| ScholarGate数据集 ↗ |
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