手法を比較

	ロバスト能動学習	Few-shot Learning
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2006	2011–2017
提唱者	Balcan, M.-F.; Beygelzimer, A.; Langford, J.	Lake, B. M.; Vinyals, O.; Finn, C. et al.
種類	Active learning with robustness guarantees	Meta-learning / low-data learning paradigm
原典	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.	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.
別名	RAL, noise-tolerant active learning, robust query learning, adversarially robust active learning	FSL, low-shot learning, k-shot learning, meta-learning for few examples
関連	6	4
概要	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.