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| وحدة البوابات المتكررة المتكيفة مع المجال (Domain-Adaptive GRU)× | وحدة البوابات المتكررة المُحسَّنة (Fine-Tuned GRU)× | |
|---|---|---|
| المجال | التعلم العميق | التعلم العميق |
| العائلة | Machine learning | Machine learning |
| سنة النشأة≠ | 2016–present | 2014 (GRU); fine-tuning practice established 2010s |
| صاحب الطريقة≠ | Cho et al. (GRU, 2014); Ganin et al. (domain-adversarial framework, 2016) | Cho, K. et al. (GRU); fine-tuning practice from transfer learning literature |
| النوع≠ | Sequence model with domain adaptation | Sequence model with transfer learning |
| المصدر التأسيسي≠ | 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. link ↗ | 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. link ↗ |
| الأسماء البديلة | DA-GRU, Domain-Adapted GRU, GRU with Domain Adaptation, Domain-Shift-Robust GRU | Fine-Tuned GRU, GRU Fine-Tuning, Domain-Adapted GRU, GRU Transfer Learning |
| ذات صلة≠ | 4 | 5 |
| الملخص≠ | 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. | 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. |
| ScholarGateمجموعة البيانات ↗ |
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