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	Sieci neuronowe typu Siamese	Uczenie transferowe
Dziedzina	Uczenie głębokie	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	1993	2010 (formalized); 1990s (early roots)
Twórca	Jane Bromley & Yann LeCun et al.; popularized by Koch et al.	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Typ	Deep metric-learning architecture	Learning paradigm
Źródło pierwotne	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.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Inne nazwy	twin network, Siamese neural network, contrastive metric network, Siyam ağı	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Pokrewne	1	3
Podsumowanie	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.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.