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| 도메인 적응 순환 신경망× | 순환 신경망(Recurrent Neural Network)을 이용한 전이 학습× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2010s | 2010 (TL survey); RNN: 1986 |
| 창시자≠ | Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs) | Pan, S. J. & Yang, Q. (transfer learning survey); RNN origins: Rumelhart, D. E. et al. (1986) |
| 유형≠ | Domain-adaptive sequential model | Transfer learning on sequence model |
| 원전≠ | Ganin, Y., Ustunova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35. link ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| 별칭 | DA-RNN, domain-adaptive RNN, domain-adapted recurrent network, cross-domain RNN | TL-RNN, Pretrained RNN, RNN Transfer Learning, Recurrent Transfer Learning |
| 관련≠ | 6 | 5 |
| 요약≠ | A Domain-adaptive Recurrent Neural Network (DA-RNN) is a recurrent neural network trained on a source domain and adapted to a target domain using domain adaptation techniques such as adversarial training, feature alignment, or fine-tuning. It enables sequential models to generalise across domains when labeled target-domain data is scarce or unavailable. | Transfer Learning with Recurrent Neural Network (TL-RNN) reuses weights learned by an RNN on a large source task — such as language modelling or sequence prediction — and adapts them to a new, often smaller target task. This strategy lets practitioners obtain strong sequence-modelling performance without the need for massive labelled datasets. |
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