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| 약지도 트랜스포머× | Self-supervised Transformer× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도 | 2017–2019 | 2017–2019 |
| 창시자≠ | Multiple contributors (weak supervision paradigm: Zhou 2018; transformer backbone: Vaswani et al. 2017) | Vaswani et al. (architecture); Devlin et al. (BERT self-supervised paradigm) |
| 유형≠ | Weakly supervised deep learning | Self-supervised deep learning model |
| 원전≠ | Ratner, A., Bach, S. H., Ehrenberg, H., Fries, J., Wu, S., & Re, C. (2017). Snorkel: Rapid training data creation with weak supervision. Proceedings of the VLDB Endowment, 11(3), 269–282. DOI ↗ | 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. DOI ↗ |
| 별칭 | WST, weakly supervised attention model, noisy-label transformer, weak supervision with transformers | SSL Transformer, self-supervised pretraining, masked self-attention pretraining, contrastive transformer |
| 관련 | 5 | 5 |
| 요약≠ | Weakly Supervised Transformer combines the representational power of Transformer architectures with weak supervision strategies that exploit noisy, incomplete, or programmatically generated labels — making it possible to train high-quality NLP and vision models when fully annotated datasets are scarce or prohibitively expensive to produce. | 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. |
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