השוואת שיטות

	חיפוש ארכיטקטורות נוירוניות	זיקוק ידע (Knowledge Distillation)
תחום	למידה עמוקה	למידה עמוקה
משפחה	Machine learning	Machine learning
שנת המקור	2017	2015
הוגה השיטה	Zoph, B. & Le, Q.V.	Hinton, G., Vinyals, O. & Dean, J.
סוג	Automated architecture optimization (deep learning)	Neural network compression (teacher–student)
מקור מכונן	Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR.	Hinton, G., Vinyals, O. & Dean, J. (2015). Distilling the Knowledge in a Neural Network. NeurIPS Deep Learning Workshop.
כינויים	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search	Bilgi Damıtma (Knowledge Distillation), bilgi damıtma, teacher-student distillation, model distillation
קשורות	5	5
תקציר	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.