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| Few-shot Learning× | Selvovervåget læring× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2011–2017 | 2018–2020 |
| Ophavsperson≠ | Lake, B. M.; Vinyals, O.; Finn, C. et al. | LeCun, Y. and community (formalized ~2018–2020) |
| Type≠ | Meta-learning / low-data learning paradigm | Representation learning paradigm |
| Oprindelig kilde≠ | 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 ↗ |
| Aliasser | FSL, low-shot learning, k-shot learning, meta-learning for few examples | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning |
| Relaterede≠ | 4 | 3 |
| Resumé≠ | 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. |
| ScholarGateDatasæt ↗ |
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