Módszerek összehasonlítása
Tekintse át a kiválasztott módszereket egymás mellett; az eltérő sorok kiemelve jelennek meg.
| Neuronális Architektúra Keresés× | A tudásdesztilláció× | |
|---|---|---|
| Tudományterület | Mélytanulás | Mélytanulás |
| Módszercsalád | Machine learning | Machine learning |
| Keletkezés éve≠ | 2017 | 2015 |
| Megalkotó≠ | Zoph, B. & Le, Q.V. | Hinton, G., Vinyals, O. & Dean, J. |
| Típus≠ | Automated architecture optimization (deep learning) | Neural network compression (teacher–student) |
| Alapmű≠ | Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR. link ↗ | Hinton, G., Vinyals, O. & Dean, J. (2015). Distilling the Knowledge in a Neural Network. NeurIPS Deep Learning Workshop. link ↗ |
| Alternatív nevek | Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search | Bilgi Damıtma (Knowledge Distillation), bilgi damıtma, teacher-student distillation, model distillation |
| Kapcsolódó | 5 | 5 |
| Összefoglaló≠ | Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All. | 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. |
| ScholarGateAdatkészlet ↗ |
|
|