方法证据记录
Domain-adaptive GRU
Domain-Adaptive GRU combines the Gated Recurrent Unit architecture with domain adaptation techniques to train a sequence model on a labeled source domain and transfer it to a different but related target domain, reducing performance degradation caused by distribution shift. It is widely applied in NLP tasks such as cross-domain sentiment analysis, named entity recognition, and text classification where labeled target-domain data is scarce.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Domain-Adaptive Gated Recurrent Unit Network
分类方法记录 · ml-model / deep-learning
- 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). Association for Computational Linguistics. · URL
- Ganin, Y., Ustinova, 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(1), 2096–2030. · URL
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