Σύγκριση μεθόδων

	Μπεϋζιανή Μάθηση Ελάχιστων Δεδομένων	Ημι-εποπτευόμενη μάθηση λίγων δειγμάτων
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2018-2019	2018
Δημιουργός	Gordon et al.; Finn, Xu & Levine	Ren, M. et al. (ICLR 2018); builds on Finn et al. (MAML, 2017)
Τύπος	Probabilistic meta-learning	Meta-learning with unlabeled auxiliary data
Θεμελιώδης πηγή	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).	Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., & Zemel, R. S. (2018). Meta-learning for semi-supervised few-shot classification. In International Conference on Learning Representations (ICLR 2018).
Εναλλακτικές ονομασίες	Bayesian meta-learning, probabilistic few-shot learning, amortized Bayesian few-shot learning, Bayesian FSL	SS-FSL, semi-supervised meta-learning, few-shot learning with unlabeled data, low-label few-shot learning
Συναφείς	5	4
Σύνοψη	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.	Semi-supervised Few-shot Learning (SS-FSL) trains models to classify new classes from only a handful of labeled examples per class, while simultaneously leveraging a pool of unlabeled data to enrich class representations. By combining meta-learning episodes with soft pseudo-label assignment for unlabeled samples, it achieves notably higher accuracy than purely supervised few-shot methods when abundant unlabeled data is available.