Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Destilación de Conocimiento× | Búsqueda de Arquitecturas Neuronales× | |
|---|---|---|
| Campo | Aprendizaje profundo | Aprendizaje profundo |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2015 | 2017 |
| Autor original≠ | Hinton, G., Vinyals, O. & Dean, J. | Zoph, B. & Le, Q.V. |
| Tipo≠ | Neural network compression (teacher–student) | Automated architecture optimization (deep learning) |
| Fuente seminal≠ | Hinton, G., Vinyals, O. & Dean, J. (2015). Distilling the Knowledge in a Neural Network. NeurIPS Deep Learning Workshop. link ↗ | Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR. link ↗ |
| Alias | Bilgi Damıtma (Knowledge Distillation), bilgi damıtma, teacher-student distillation, model distillation | Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search |
| Relacionados | 5 | 5 |
| Resumen≠ | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
|
|