Comparer des méthodes

	Apprentissage par transfert avec réseau neuronal récurrent	Réseau neuronal récurrent affiné
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2010 (TL survey); RNN: 1986	2015–2018
Auteur d'origine	Pan, S. J. & Yang, Q. (transfer learning survey); RNN origins: Rumelhart, D. E. et al. (1986)	Popularised by Howard & Ruder (ULMFiT, 2018); RNN fine-tuning concept developed iteratively in the NLP community from ~2015
Type	Transfer learning on sequence model	Transfer learning / sequential model adaptation
Source fondatrice	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	Howard, J. & Ruder, S. (2018). Universal Language Model Fine-Tuning for Text Classification. Proceedings of ACL 2018, 328–339.
Alias	TL-RNN, Pretrained RNN, RNN Transfer Learning, Recurrent Transfer Learning	Fine-Tuned RNN, RNN Fine-Tuning, domain-adapted RNN, pre-trained RNN with downstream adaptation
Apparentées	5	6
Résumé	Transfer Learning with Recurrent Neural Network (TL-RNN) reuses weights learned by an RNN on a large source task — such as language modelling or sequence prediction — and adapts them to a new, often smaller target task. This strategy lets practitioners obtain strong sequence-modelling performance without the need for massive labelled datasets.	A Fine-Tuned Recurrent Neural Network (RNN) starts from a model pre-trained on large corpora or time-series data and adapts its weights to a specific downstream task through controlled gradient updates. The approach dramatically cuts the labeled data needed for strong sequence modeling performance in text classification, named entity recognition, sentiment analysis, and related tasks.