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| Neuronale Architektursuche× | Wissensdestillation× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2017 | 2015 |
| Urheber≠ | Zoph, B. & Le, Q.V. | Hinton, G., Vinyals, O. & Dean, J. |
| Typ≠ | Automated architecture optimization (deep learning) | Neural network compression (teacher–student) |
| Wegweisende Quelle≠ | 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 ↗ |
| Aliasnamen | Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search | Bilgi Damıtma (Knowledge Distillation), bilgi damıtma, teacher-student distillation, model distillation |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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. |
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