方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 弱监督 LSTM× | 弱监督 Transformer× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2016–2018 | 2017–2019 |
| 提出者≠ | Ratner et al. (data programming framework); Hochreiter & Schmidhuber (LSTM backbone) | Multiple contributors (weak supervision paradigm: Zhou 2018; transformer backbone: Vaswani et al. 2017) |
| 类型≠ | Weakly supervised sequence model | Weakly supervised deep learning |
| 开创性文献≠ | 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 ↗ |
| 别名 | WS-LSTM, noisy-label LSTM, distant-supervision LSTM, data-programming LSTM | WST, weakly supervised attention model, noisy-label transformer, weak supervision with transformers |
| 相关≠ | 6 | 5 |
| 摘要≠ | Weakly supervised LSTM trains a Long Short-Term Memory network on sequence data where clean, manually annotated labels are scarce or absent. Instead, multiple imperfect label sources — heuristic rules, distant supervision, crowdsourcing, or programmatic labeling functions — are combined to produce probabilistic training labels, which are then used to supervise the LSTM. This allows scalable training on large unlabeled corpora without exhaustive human annotation. | 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. |
| ScholarGate数据集 ↗ |
|
|