Comparer des méthodes

	Apprentissage multi-tâches	Distillation de connaissances	Apprentissage par transfert
Domaine	Apprentissage profond	Apprentissage profond	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	1997	2015	2010 (formalized); 1990s (early roots)
Auteur d'origine	Rich Caruana	Hinton, G., Vinyals, O. & Dean, J.	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Type	Inductive transfer method	Neural network compression (teacher–student)	Learning paradigm
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.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Alias	MTL, Joint Learning, Shared Representation Learning, Çok Görevli Öğrenme	Bilgi Damıtma (Knowledge Distillation), bilgi damıtma, teacher-student distillation, model distillation	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Apparentées	3	5	3
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.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.