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| 약한 지도 순환 신경망× | 약지도 트랜스포머× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2009–2016 | 2017–2019 |
| 창시자≠ | Broadly attributed to the weak supervision / distant supervision research community (Mintz et al., 2009; Ratner et al., 2016) | Multiple contributors (weak supervision paradigm: Zhou 2018; transformer backbone: Vaswani et al. 2017) |
| 유형≠ | Supervised learning under noisy or incomplete labels | Weakly supervised deep learning |
| 원전≠ | Ratner, A., De Sa, C., Wu, S., Selsam, D., & Re, C. (2016). Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems (NeurIPS), 29. link ↗ | 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 ↗ |
| 별칭 | WS-RNN, distantly supervised RNN, noise-tolerant RNN, weakly supervised sequence model | WST, weakly supervised attention model, noisy-label transformer, weak supervision with transformers |
| 관련 | 5 | 5 |
| 요약≠ | A weakly supervised RNN trains a recurrent neural network on sequences whose labels come from imperfect sources — heuristic rules, distant supervision, crowdsourcing, or generative label models — rather than expensive expert annotation. This lets researchers exploit large unlabeled corpora for sequential tasks such as text classification, named entity recognition, or time-series prediction when fully annotated data is scarce or costly. | 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. |
| ScholarGate데이터셋 ↗ |
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