Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Ufumbuzi wa Maarifa× | Kujifunza kwa uhamishaji× | |
|---|---|---|
| Nyanja≠ | Ujifunzaji wa Kina | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2015 | 2010 (formalized); 1990s (early roots) |
| Mwanzilishi≠ | Hinton, G., Vinyals, O. & Dean, J. | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Aina≠ | Neural network compression (teacher–student) | Learning paradigm |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala | Bilgi Damıtma (Knowledge Distillation), bilgi damıtma, teacher-student distillation, model distillation | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Zinazohusiana≠ | 5 | 3 |
| Muhtasari≠ | 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. |
| ScholarGateSeti ya data ↗ |
|
|