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| Siiami närvivõrk× | Ülekandeõpe× | |
|---|---|---|
| Valdkond≠ | Süvaõpe | Masinõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 1993 | 2010 (formalized); 1990s (early roots) |
| Looja≠ | Jane Bromley & Yann LeCun et al.; popularized by Koch et al. | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Tüüp≠ | Deep metric-learning architecture | Learning paradigm |
| Algallikas≠ | 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. link ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Rööpnimetused | twin network, Siamese neural network, contrastive metric network, Siyam ağı | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Seotud≠ | 1 | 3 |
| Kokkuvõte≠ | 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. |
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