方法对比
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| 自监督少样本学习× | 迁移学习× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2019 | 2010 (formalized); 1990s (early roots) |
| 提出者≠ | Gidaris, S. et al.; Su, J.-C. et al. (concurrent seminal works) | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| 类型≠ | Hybrid learning paradigm (self-supervised pretraining + few-shot adaptation) | Learning paradigm |
| 开创性文献≠ | Gidaris, S., Bursuc, A., Komodakis, N., Perez, P., & Cord, M. (2019). Boosting Few-Shot Visual Learning with Self-Supervision. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 8059–8068. DOI ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| 别名 | SSL-FSL, self-supervised meta-learning, unsupervised few-shot learning, self-supervised prototypical learning | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| 相关≠ | 2 | 3 |
| 摘要≠ | Self-supervised Few-shot Learning (SSL-FSL) combines self-supervised pretraining on large unlabeled corpora with few-shot meta-learning so that a model can recognize new categories from only a handful of labeled examples. By learning rich, transferable representations without expensive annotation, SSL-FSL addresses the fundamental bottleneck of supervised few-shot methods: the need for labeled support data at scale. | 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. |
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