Comparer des méthodes

	Apprentissage multi-tâches	Distillation de connaissances
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	1997	2015
Auteur d'origine	Rich Caruana	Hinton, G., Vinyals, O. & Dean, J.
Type	Inductive transfer method	Neural network compression (teacher–student)
Source fondatrice	Caruana, R. (1997). Multitask learning. Machine Learning, 28(1), 41–75.	Hinton, G., Vinyals, O. & Dean, J. (2015). Distilling the Knowledge in a Neural Network. NeurIPS Deep Learning Workshop.
Alias	MTL, Joint Learning, Shared Representation Learning, Çok Görevli Öğrenme	Bilgi Damıtma (Knowledge Distillation), bilgi damıtma, teacher-student distillation, model distillation
Apparentées	3	5
Résumé	Multitask Learning (MTL) is a machine learning paradigm in which a model is trained simultaneously on multiple related tasks, sharing representations across them to improve generalization. Introduced formally by Rich Caruana in 1997, MTL draws on the intuition that auxiliary tasks act as inductive bias, providing extra supervision signals that help the shared layers learn richer, more robust feature representations than single-task training would yield.	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.