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| Εύρωστη Ενεργή Μάθηση× | Εκμάθηση με λίγα δείγματα× | |
|---|---|---|
| Πεδίο | Μηχανική Μάθηση | Μηχανική Μάθηση |
| Οικογένεια | Machine learning | Machine learning |
| Έτος προέλευσης≠ | 2006 | 2011–2017 |
| Δημιουργός≠ | Balcan, M.-F.; Beygelzimer, A.; Langford, J. | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| Τύπος≠ | Active learning with robustness guarantees | Meta-learning / low-data learning paradigm |
| Θεμελιώδης πηγή≠ | Balcan, M.-F., Beygelzimer, A., & Langford, J. (2006). Agnostic active learning. In Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), pp. 65–72. ACM. DOI ↗ | 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 ↗ |
| Εναλλακτικές ονομασίες | RAL, noise-tolerant active learning, robust query learning, adversarially robust active learning | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| Συναφείς≠ | 6 | 4 |
| Σύνοψη≠ | Robust Active Learning extends the standard active learning framework to handle noisy labels, adversarial perturbations, and unreliable oracles. Rather than assuming perfect labeling, it incorporates statistical or adversarial robustness guarantees into the query selection process, maintaining sample efficiency while tolerating corruption in the annotation process. | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
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