手法を比較

	自己教師あり距離学習	距離学習
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2020 (modern contrastive formulation); foundations 1990s–2000s	2003 (foundational); refined 2009 (LMNN)
提唱者	Chen, T. et al. (SimCLR); earlier metric learning foundations by Bromley, LeCun (1994)	Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.
種類	Self-supervised representation learning with metric objective	Representation learning / supervised distance optimization
原典	Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning (ICML 2020), 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.
別名	self-supervised representation learning with metric loss, contrastive self-supervised learning, unsupervised metric learning, SSML	Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance
関連	3	5
概要	Self-supervised metric learning trains a neural encoder to embed inputs so that semantically similar items lie close together in vector space, using automatically generated pseudo-labels instead of human annotations. By combining self-supervised pretext tasks with contrastive or triplet-based metric objectives, it produces transferable, label-efficient representations applicable to retrieval, clustering, and few-shot classification.	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.