পদ্ধতির তুলনা করুন

	ডোমেইন-অ্যাডাপ্টিভ রিকারেন্ট নিউরাল নেটওয়ার্ক	ডোমেইন-অ্যাডাপ্টিভ BERT-ভিত্তিক ক্লাসিফিকেশন
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2010s	2019–2020
প্রবর্তক	Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs)	Gururangan et al. (2020); earlier domain-specific instances include Lee et al. (2020) — BioBERT
ধরন	Domain-adaptive sequential model	Domain-adaptive pre-training followed by supervised fine-tuning
মৌলিক উৎস	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.	Gururangan, S., Marasovic, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020). Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 8342–8360.
অপর নাম	DA-RNN, domain-adaptive RNN, domain-adapted recurrent network, cross-domain RNN	DAPT BERT classification, domain-adaptive pre-training, domain-specific BERT fine-tuning, BERT DAPT
সম্পর্কিত	6	6
সারসংক্ষেপ	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.	Domain-adaptive BERT-based classification extends the standard fine-tuning pipeline by first continuing BERT's masked-language-model pre-training on a large corpus of in-domain unlabeled text, then fine-tuning the adapted model on labeled examples for the target classification task. This two-stage approach closes the vocabulary and distributional gap between BERT's general pre-training corpus and specialized domains such as biomedicine, law, finance, or social-media text.