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Domain-adaptive Recurrent Neural Network

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.

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Sources

  1. 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 ↗
  2. Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI: 10.1109/TKDE.2009.191 ↗

Related methods

Domain-adaptive BERT-based ClassificationDomain-adaptive transformerFine-Tuned Recurrent Neural NetworkLong Short-Term MemoryRecurrent Neural NetworkTransfer Learning with Recurrent Neural Network

Referenced by

Domain-adaptive Convolutional Neural NetworkDomain-adaptive GRUDomain-adaptive Multilayer Perceptron

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ScholarGate — Domain-adaptive Recurrent Neural Network (Domain-adaptive Recurrent Neural Network (DA-RNN)). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/domain-adaptive-recurrent-neural-network