So sánh phương pháp

	GRU bán giám sát	GRU tự giám sát
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2014–2015	2014–2019
Người khởi xướng	Dai, A. M. & Le, Q. V. (semi-supervised sequence learning); Cho, K. et al. (GRU architecture)	Cho, K. et al. (GRU); self-supervised training paradigm from broader SSL literature
Loại	Semi-supervised sequence model	Self-supervised sequence model
Công trình gốc	Dai, A. M., & Le, Q. V. (2015). Semi-supervised Sequence Learning. Advances in Neural Information Processing Systems (NeurIPS), 28.	Cho, K., van Merriënboer, 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.
Tên gọi khác	Semi-supervised GRU, SSL-GRU, GRU with unlabeled data, semi-supervised recurrent classifier	SS-GRU, Self-supervised Gated Recurrent Unit, GRU with self-supervised pretraining, Unsupervised GRU pretraining
Liên quan	5	4
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.	Self-supervised GRU trains a Gated Recurrent Unit network using automatically constructed supervision signals — such as next-step prediction or masked token recovery — derived from the unlabeled data itself. The learned sequence representations are then fine-tuned on small labeled datasets, making high-quality sequential modeling feasible when annotations are scarce.