Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Tiešsaistes dažu šāvienu mācīšanās× | Pārneses apmācība× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2019 | 2010 (formalized); 1990s (early roots) |
| Autors≠ | Finn, C. et al. (online meta-learning formalization) | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Tips≠ | Online learning + meta-learning hybrid | Learning paradigm |
| Pirmavots≠ | Finn, C., Rajeswaran, A., Kakade, S., & Levine, S. (2019). Online Meta-Learning. Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97, 1920–1930. link ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Citi nosaukumi | online meta-learning, streaming few-shot learning, continual few-shot learning, incremental few-shot learning | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Saistītās≠ | 4 | 3 |
| Kopsavilkums≠ | Online Few-shot Learning combines the streaming update principle of online learning with the data-efficiency goal of few-shot learning, enabling a model to continuously adapt to new tasks or classes from only a handful of labeled examples as data arrives sequentially — without access to the full historical dataset. | 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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