Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Дистиляція знань× | Трансферне навчання× | |
|---|---|---|
| Галузь≠ | Глибоке навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2015 | 2010 (formalized); 1990s (early roots) |
| Автор методу≠ | Hinton, G., Vinyals, O. & Dean, J. | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Тип≠ | Neural network compression (teacher–student) | Learning paradigm |
| Основоположне джерело≠ | Hinton, G., Vinyals, O. & Dean, J. (2015). Distilling the Knowledge in a Neural Network. NeurIPS Deep Learning Workshop. link ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Інші назви | Bilgi Damıtma (Knowledge Distillation), bilgi damıtma, teacher-student distillation, model distillation | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Пов'язані≠ | 5 | 3 |
| Підсумок≠ | Knowledge Distillation is a model-compression technique, introduced by Geoffrey Hinton and colleagues in 2015, that trains a small student model using the soft-label outputs of a large teacher model. Distilled models such as DistilBERT and TinyBERT reach roughly 97% of the larger model's performance while running far faster. | 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. |
| ScholarGateНабір даних ↗ |
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