Compară metode

	Învățarea metricilor robuste	Învățare cu puține exemple
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2009–2012	2011–2017
Autorul original	Various (Weinberger, Saul, Schultz et al.; robust extensions by Shen, Cao and others, 2009–2012)	Lake, B. M.; Vinyals, O.; Finn, C. et al.
Tip	Supervised/semi-supervised distance metric learning with robustness to noise and outliers	Meta-learning / low-data learning paradigm
Sursa seminală	Shen, C., Kim, J., Wang, L., & van den Hengel, A. (2012). Positive Semidefinite Metric Learning Using Boosting-like Algorithms. Journal of Machine Learning Research, 13, 1007–1036.	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.
Denumiri alternative	robust distance metric learning, noise-robust metric learning, outlier-robust similarity learning, robust DML	FSL, low-shot learning, k-shot learning, meta-learning for few examples
Înrudite	5	4
Rezumat	Robust Metric Learning learns a Mahalanobis distance function from labeled or pairwise-constrained data while actively resisting the distortion caused by noisy labels, corrupted examples, or outliers. By replacing standard hinge or squared losses with robust alternatives and adding regularization, it produces a distance metric that generalises well even when the training set is imperfect — a common situation in real-world scientific and applied tasks.	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.