Võrdle meetodeid
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| Poolitult järelevalve all toimuv föderatiivne õpe× | Väheste näidistega õppimine× | |
|---|---|---|
| Valdkond | Masinõpe | Masinõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2020 | 2011–2017 |
| Looja≠ | Jeong, W. et al. / multiple independent groups | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| Tüüp≠ | Distributed semi-supervised learning framework | Meta-learning / low-data learning paradigm |
| Algallikas≠ | Jeong, W., Yoon, J., Yang, E., & Hwang, S. J. (2020). Federated Semi-Supervised Learning with Inter-Client Consistency. International Conference on Learning Representations (ICLR 2021). link ↗ | 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 ↗ |
| Rööpnimetused | SSL-FL, federated semi-supervised learning, FSSL, semi-supervised distributed learning | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| Seotud≠ | 6 | 4 |
| Kokkuvõte≠ | Semi-supervised federated learning (SSFL) trains a shared model across many decentralized clients — each holding private data — when only a subset of clients or a subset of local samples carry labels. It combines the privacy-preserving coordination of federated learning with the label-efficiency of semi-supervised techniques such as pseudo-labeling and consistency regularization, enabling strong model quality without centralizing sensitive data. | 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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