Salīdzināt metodes

	Pusuzraudzīts transformators	Pašuzraudzības Transformer
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2018–2019	2017–2019
Autors	Devlin, J. et al. (BERT); broader SSL-Transformer paradigm community	Vaswani et al. (architecture); Devlin et al. (BERT self-supervised paradigm)
Tips	Semi-supervised deep learning	Self-supervised deep learning model
Pirmavots	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.	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.
Citi nosaukumi	semi-supervised transformer model, SSL transformer, transformer with self-supervised pre-training, semi-supervised attention model	SSL Transformer, self-supervised pretraining, masked self-attention pretraining, contrastive transformer
Saistītās	5	5
Kopsavilkums	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.	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.