방법 비교

	자기 지도 GRU	Self-supervised Transformer
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2014–2019	2017–2019
창시자	Cho, K. et al. (GRU); self-supervised training paradigm from broader SSL literature	Vaswani et al. (architecture); Devlin et al. (BERT self-supervised paradigm)
유형	Self-supervised sequence model	Self-supervised deep learning model
원전	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.	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.
별칭	SS-GRU, Self-supervised Gated Recurrent Unit, GRU with self-supervised pretraining, Unsupervised GRU pretraining	SSL Transformer, self-supervised pretraining, masked self-attention pretraining, contrastive transformer
관련	4	5
요약	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.	A self-supervised Transformer is a Transformer network pretrained using automatically constructed supervision signals — such as masked token prediction or next-sentence prediction — rather than human-annotated labels. The resulting representations are then fine-tuned or probed on downstream tasks. BERT, GPT, and ViT (Vision Transformer in masked-image modeling mode) are the most widely known instantiations of this paradigm.