So sánh phương pháp

	Học đo lường tự giám sát	Mạng nơ-ron Siamese
Lĩnh vực	Học máy	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2020 (modern contrastive formulation); foundations 1990s–2000s	1993
Người khởi xướng	Chen, T. et al. (SimCLR); earlier metric learning foundations by Bromley, LeCun (1994)	Jane Bromley & Yann LeCun et al.; popularized by Koch et al.
Loại	Self-supervised representation learning with metric objective	Deep metric-learning architecture
Công trình gốc	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.	Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., & Shah, R. (1993). Signature verification using a 'Siamese' time delay neural network. Advances in Neural Information Processing Systems, 6.
Tên gọi khác	self-supervised representation learning with metric loss, contrastive self-supervised learning, unsupervised metric learning, SSML	twin network, Siamese neural network, contrastive metric network, Siyam ağı
Liên quan	3	1
Tóm tắt	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.	A Siamese network is a deep architecture with two (or more) identical, weight-sharing branches that map inputs into an embedding space where similar inputs land close together and dissimilar ones far apart. Introduced by Bromley, LeCun, and colleagues in 1993 for signature verification and revived by Koch et al. (2015) for one-shot image recognition, it learns a similarity metric rather than fixed class labels, making it ideal for verification, matching, and few-shot tasks.