পদ্ধতির তুলনা করুন

	Robust Active Learning	ফিউ-শট লার্নিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2006	2011–2017
প্রবর্তক	Balcan, M.-F.; Beygelzimer, A.; Langford, J.	Lake, B. M.; Vinyals, O.; Finn, C. et al.
ধরন	Active learning with robustness guarantees	Meta-learning / low-data learning paradigm
মৌলিক উৎস	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.
অপর নাম	RAL, noise-tolerant active learning, robust query learning, adversarially robust active learning	FSL, low-shot learning, k-shot learning, meta-learning for few examples
সম্পর্কিত	6	4
সারসংক্ষেপ	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.