Salīdzināt metodes

	Pielāgots LSTM	Pielāgotais GRU
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2018 (fine-tuning paradigm formalised); LSTM core: 1997	2014 (GRU); fine-tuning practice established 2010s
Autors	Howard, J. & Ruder, S. (ULMFiT); foundational LSTM by Hochreiter & Schmidhuber	Cho, K. et al. (GRU); fine-tuning practice from transfer learning literature
Tips	Supervised sequential model with transfer learning	Sequence model with transfer learning
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.	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.
Citi nosaukumi	Fine-Tuned LSTM, LSTM Fine-Tuning, Pre-trained LSTM with Task Adaptation, LSTM Transfer Learning	Fine-Tuned GRU, GRU Fine-Tuning, Domain-Adapted GRU, GRU Transfer Learning
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.	Fine-Tuned GRU adapts a Gated Recurrent Unit network — pre-trained on a large source dataset — to a specific target task or domain by continuing training on domain-specific labeled data. This combines the sequential memory capacity of GRUs with the efficiency gains of transfer learning, achieving strong performance even when labeled target data is scarce.