Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Обучение по учебному плану× | Перенос обучения× | |
|---|---|---|
| Область≠ | Глубокое обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2009 | 2010 (formalized); 1990s (early roots) |
| Автор метода≠ | Yoshua Bengio et al. | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Тип≠ | Training strategy | Learning paradigm |
| Основополагающий источник≠ | Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009). Curriculum learning. International Conference on Machine Learning (ICML), 41–48. DOI ↗ | 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 | Scheduled Training, Difficulty-Based Training, Self-Paced Learning, Müfredat Öğrenimi | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Связанные | 3 | 3 |
| Сводка≠ | Curriculum Learning is a training strategy for machine learning models, introduced by Bengio et al. in 2009, in which training examples are presented in a meaningful order—typically from easy to hard—rather than at random. Inspired by how humans and animals learn progressively, it organizes training data into a curriculum that starts with simpler, cleaner, or more representative samples and gradually introduces harder or more complex examples as the model matures. | 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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