مقایسهٔ روش‌ها

	یادگیری انتقالی با شبکه عصبی بازگشتی	واحد تکرارشونده گیت‌دار (GRU)
حوزه	یادگیری عمیق	یادگیری عمیق
خانواده	Machine learning	Machine learning
سال پیدایش	2010 (TL survey); RNN: 1986	2014
پدیدآور	Pan, S. J. & Yang, Q. (transfer learning survey); RNN origins: Rumelhart, D. E. et al. (1986)	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y.
نوع	Transfer learning on sequence model	Recurrent neural network with gating
منبع بنیادین	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	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.
نام‌های دیگر	TL-RNN, Pretrained RNN, RNN Transfer Learning, Recurrent Transfer Learning	GRU, GRU network, gated RNN, GRU cell
مرتبط	5	3
خلاصه	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.	The Gated Recurrent Unit (GRU), introduced by Cho et al. in 2014, is a streamlined recurrent neural network that uses two learned gates — an update gate and a reset gate — to selectively retain or discard information across time steps, enabling effective sequence modelling with fewer parameters than LSTM.