השוואת שיטות

	K-שכנים הקרובים (K-nearest neighbors) בלמידה מונחית-עצמית	למידת מטריקות
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2018–2020	2003 (foundational); refined 2009 (LMNN)
הוגה השיטה	Wu, Z. et al. / Chen, T. et al.	Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.
סוג	Self-supervised + non-parametric classifier	Representation learning / supervised distance optimization
מקור מכונן	Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119, 1597–1607.	Xing, E. P., Jordan, M. I., Russell, S., & Ng, A. Y. (2003). Distance metric learning with application to clustering with side-information. In Advances in Neural Information Processing Systems (NIPS), 16, 505–512.
כינויים	SSL-kNN, self-supervised kNN classifier, kNN evaluation probe, nearest-neighbor self-supervised classifier	Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance
קשורות	4	5
תקציר	Self-supervised K-nearest neighbors (SSL-kNN) combines representation learning without labels with a non-parametric k-NN classifier. A neural encoder is first trained via a self-supervised objective — such as contrastive or masked prediction — so that semantically similar samples cluster together in the embedding space. A simple k-NN lookup on those embeddings then assigns class labels, serving both as a lightweight probe and as a practical classifier.	Metric learning is a machine-learning framework that trains a distance or similarity function from data so that semantically similar examples end up close together in the learned space while dissimilar examples are pushed apart. Unlike fixed distances such as Euclidean, the learned metric adapts to the structure of the task, making downstream classifiers, clusterers, and retrieval systems significantly more accurate.