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Transfer Learning with Recurrent Neural Network

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.

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Sources

  1. Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI: 10.1109/TKDE.2009.191 ↗
  2. Transfer learning. Wikipedia. link ↗

Related methods

Fine-Tuned Recurrent Neural NetworkGated Recurrent UnitLong Short-Term MemoryRecurrent Neural NetworkTransfer Learning with LSTM

Referenced by

Domain-adaptive Recurrent Neural NetworkFine-Tuned Recurrent Neural NetworkTransfer Learning with LSTM

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ScholarGate — Transfer Learning with Recurrent Neural Network (Transfer Learning with Recurrent Neural Network (TL-RNN)). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/transfer-learning-with-recurrent-neural-network