Salīdzināt metodes

	Pielāgots LSTM	Pārneses mācīšanās ar LSTM
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2018 (fine-tuning paradigm formalised); LSTM core: 1997	2018 (ULMFiT; concept since ~2010)
Autors	Howard, J. & Ruder, S. (ULMFiT); foundational LSTM by Hochreiter & Schmidhuber	Howard, J. & Ruder, S. (ULMFiT); general concept: Pan & Yang (2010)
Tips	Supervised sequential model with transfer learning	Transfer learning / Sequential model
Pirmavots	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.	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.
Citi nosaukumi	Fine-Tuned LSTM, LSTM Fine-Tuning, Pre-trained LSTM with Task Adaptation, LSTM Transfer Learning	LSTM Transfer Learning, Pre-trained LSTM, LSTM Fine-Tuning, ULMFiT-style LSTM Transfer
Saistītās	6	5
Kopsavilkums	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.	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.