Sammenlign metoder

	Finjusteret GRU	Fintunet Transformer
Fagområde	Dyb læring	Dyb læring
Familie	Machine learning	Machine learning
Oprindelsesår	2014 (GRU); fine-tuning practice established 2010s	2017–2019
Ophavsperson	Cho, K. et al. (GRU); fine-tuning practice from transfer learning literature	Vaswani et al. (architecture); fine-tuning paradigm popularised by Howard & Ruder, Devlin et al.
Type	Sequence model with transfer learning	Transfer learning / supervised fine-tuning
Oprindelig kilde	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.	Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Aliasser	Fine-Tuned GRU, GRU Fine-Tuning, Domain-Adapted GRU, GRU Transfer Learning	Transformer fine-tuning, pre-trained transformer fine-tuning, task-adaptive transformer, downstream-tuned transformer
Relaterede	5	4
Resumé	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.	Fine-tuning a Transformer adapts a large pre-trained model — such as BERT, GPT, or ViT — to a specific downstream task by continuing gradient-based training on a labelled target dataset. This two-stage paradigm (pre-train then fine-tune) consistently achieves state-of-the-art results across NLP and computer vision tasks with far less task-specific data than training from scratch.