Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Învățare Bayesiană cu Puține Exemple (Few-Shot Learning)× | Învățare prin transfer× | |
|---|---|---|
| Domeniu | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2018-2019 | 2010 (formalized); 1990s (early roots) |
| Autorul original≠ | Gordon et al.; Finn, Xu & Levine | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Tip≠ | Probabilistic meta-learning | Learning paradigm |
| Sursa seminală≠ | Gordon, J., Bronskill, J., Bauer, M., Nowozin, S. & Turner, R. E. (2019). Meta-Learning Probabilistic Inference for Prediction. International Conference on Learning Representations (ICLR 2019). link ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Denumiri alternative | Bayesian meta-learning, probabilistic few-shot learning, amortized Bayesian few-shot learning, Bayesian FSL | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Înrudite≠ | 5 | 3 |
| Rezumat≠ | Bayesian few-shot learning combines Bayesian inference with meta-learning to enable a model to generalize from as few as one to five labeled examples per class. By treating task-specific parameters as random variables and learning an informative prior across many training tasks, the method produces calibrated uncertainty estimates alongside predictions — a key advantage over deterministic few-shot learners. | 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. |
| ScholarGateSet de date ↗ |
|
|