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Transfer Learning with LSTM

Transfer Learning with LSTM is a technique in which a Long Short-Term Memory network is first pre-trained on a large source corpus or task, and then its learned weights are transferred and fine-tuned on a smaller target task. This approach, popularized by ULMFiT (Howard & Ruder, 2018), allows LSTM-based models to reach strong performance even when labeled target data is scarce.

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Sources

  1. 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. DOI: 10.18653/v1/P18-1031 ↗
  2. Transfer learning. Wikipedia. link ↗

Related methods

BERT-based ClassificationFine-Tuned LSTMGated Recurrent UnitLong Short-Term MemoryTransfer Learning with Recurrent Neural Network

Referenced by

Fine-Tuned LSTMTransfer Learning with Recurrent Neural Network

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ScholarGate — Transfer Learning with LSTM (Transfer Learning with Long Short-Term Memory Networks). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/transfer-learning-with-lstm