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Machine learningDeep learning / NLP / CV

Fine-Tuned LSTM

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.

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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. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. DOI: 10.1162/neco.1997.9.8.1735 ↗

Related methods

BERT-based ClassificationFine-Tuned GRUFine-Tuned Recurrent Neural NetworkFine-Tuned TransformerLong Short-Term MemoryTransfer Learning with LSTM

Referenced by

Fine-Tuned GRUFine-Tuned Multilayer PerceptronFine-Tuned Recurrent Neural NetworkTransfer Learning with LSTMWeakly supervised LSTM

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ScholarGate — Fine-Tuned LSTM (Fine-Tuned Long Short-Term Memory Network). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/fine-tuned-lstm