Comparer des méthodes

	GRU affiné	LSTM affiné
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2014 (GRU); fine-tuning practice established 2010s	2018 (fine-tuning paradigm formalised); LSTM core: 1997
Auteur d'origine	Cho, K. et al. (GRU); fine-tuning practice from transfer learning literature	Howard, J. & Ruder, S. (ULMFiT); foundational LSTM by Hochreiter & Schmidhuber
Type	Sequence model with transfer learning	Supervised sequential model with transfer learning
Source fondatrice	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014, pp. 1724-1734.	Howard, J., & Ruder, S. (2018). Universal Language Model Fine-tuning for Text Classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), 328–339.
Alias	Fine-Tuned GRU, GRU Fine-Tuning, Domain-Adapted GRU, GRU Transfer Learning	Fine-Tuned LSTM, LSTM Fine-Tuning, Pre-trained LSTM with Task Adaptation, LSTM Transfer Learning
Apparentées	5	6
Résumé	Fine-Tuned GRU adapts a Gated Recurrent Unit network — pre-trained on a large source dataset — to a specific target task or domain by continuing training on domain-specific labeled data. This combines the sequential memory capacity of GRUs with the efficiency gains of transfer learning, achieving strong performance even when labeled target data is scarce.	Fine-Tuned LSTM adapts a Long Short-Term Memory network pre-trained on a large corpus to a specific downstream task — such as text classification, sentiment analysis, or sequence labeling — by continuing training on task-specific labeled data. Popularised by the ULMFiT framework, this approach achieves strong performance even when labeled data is scarce.