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| Opmærksomhedsmekanisme× | Overførselslæring× | |
|---|---|---|
| Fagområde≠ | Dyb læring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2015 | 2010 (formalized); 1990s (early roots) |
| Ophavsperson≠ | Bahdanau, D.; Luong, M.T. | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Type≠ | Neural attention layer (encoder-decoder) | Learning paradigm |
| Oprindelig kilde≠ | Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR. link ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Aliasser≠ | Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Relaterede≠ | 5 | 3 |
| Resumé≠ | The attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector. | 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. |
| ScholarGateDatasæt ↗ |
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