手法を比較

	ファインチューニングされたGRU	リカレントニューラルネットワーク (RNN)
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2014 (GRU); fine-tuning practice established 2010s	1986–1990
提唱者	Cho, K. et al. (GRU); fine-tuning practice from transfer learning literature	Rumelhart, D. E.; Elman, J. L.
種類	Sequence model with transfer learning	Sequential neural network
原典	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.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
別名	Fine-Tuned GRU, GRU Fine-Tuning, Domain-Adapted GRU, GRU Transfer Learning	RNN, Elman network, Jordan network, simple recurrent network
関連	5	3
概要	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.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.