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| تعلم القياس الذاتي الإشرافي× | شبكة سيامي العصبية× | |
|---|---|---|
| المجال≠ | تعلم الآلة | التعلم العميق |
| العائلة | Machine learning | Machine learning |
| سنة النشأة≠ | 2020 (modern contrastive formulation); foundations 1990s–2000s | 1993 |
| صاحب الطريقة≠ | Chen, T. et al. (SimCLR); earlier metric learning foundations by Bromley, LeCun (1994) | Jane Bromley & Yann LeCun et al.; popularized by Koch et al. |
| النوع≠ | Self-supervised representation learning with metric objective | Deep metric-learning architecture |
| المصدر التأسيسي≠ | 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. link ↗ | 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 ↗ |
| الأسماء البديلة | 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 |
| ذات صلة≠ | 3 | 1 |
| الملخص≠ | 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. |
| ScholarGateمجموعة البيانات ↗ |
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