Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Механизм внимания× | Перенос обучения× | |
|---|---|---|
| Область≠ | Глубокое обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2015 | 2010 (formalized); 1990s (early roots) |
| Автор метода≠ | Bahdanau, D.; Luong, M.T. | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Тип≠ | Neural attention layer (encoder-decoder) | Learning paradigm |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия≠ | Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Связанные≠ | 5 | 3 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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