So sánh phương pháp

	GRU bán giám sát	Transformer bán giám sát
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2014–2015	2018–2019
Người khởi xướng	Dai, A. M. & Le, Q. V. (semi-supervised sequence learning); Cho, K. et al. (GRU architecture)	Devlin, J. et al. (BERT); broader SSL-Transformer paradigm community
Loại	Semi-supervised sequence model	Semi-supervised deep learning
Công trình gốc	Dai, A. M., & Le, Q. V. (2015). Semi-supervised Sequence Learning. Advances in Neural Information Processing Systems (NeurIPS), 28.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.
Tên gọi khác	Semi-supervised GRU, SSL-GRU, GRU with unlabeled data, semi-supervised recurrent classifier	semi-supervised transformer model, SSL transformer, transformer with self-supervised pre-training, semi-supervised attention model
Liên quan	5	5
Tóm tắt	Semi-supervised GRU applies the Gated Recurrent Unit architecture to settings where only a small fraction of sequential data is labeled. By first pre-training or jointly training on abundant unlabeled sequences — through language modeling, auto-encoding, or consistency regularization — and then fine-tuning on labeled examples, the model exploits the full corpus to learn richer sequence representations than supervised-only training would allow.	Semi-supervised learning with Transformer architectures leverages large quantities of unlabeled data alongside a small labeled set to train powerful sequence models. The dominant pattern — exemplified by BERT — first pre-trains the Transformer on unlabeled data using self-supervised objectives such as masked token prediction, then fine-tunes it on the labeled task. This two-stage approach dramatically reduces the labeled data needed to achieve strong performance.