방법 비교

	약한 지도 순환 신경망	약지도 학습 LSTM
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2009–2016	2016–2018
창시자	Broadly attributed to the weak supervision / distant supervision research community (Mintz et al., 2009; Ratner et al., 2016)	Ratner et al. (data programming framework); Hochreiter & Schmidhuber (LSTM backbone)
유형	Supervised learning under noisy or incomplete labels	Weakly supervised sequence model
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.	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.
별칭	WS-RNN, distantly supervised RNN, noise-tolerant RNN, weakly supervised sequence model	WS-LSTM, noisy-label LSTM, distant-supervision LSTM, data-programming LSTM
관련	5	6
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 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.