Sammenlign metoder
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| Svagt overvåget GRU× | Semi-superviseret GRU× | |
|---|---|---|
| Fagområde | Dyb læring | Dyb læring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2014–2016 | 2014–2015 |
| Ophavsperson≠ | Chung et al. (GRU); Ratner et al. (weak supervision framework) | Dai, A. M. & Le, Q. V. (semi-supervised sequence learning); Cho, K. et al. (GRU architecture) |
| Type≠ | Weakly supervised sequence model | Semi-supervised sequence model |
| Oprindelig kilde≠ | Ratner, A. J., De Sa, C. M., Wu, S., Selsam, D., & Re, C. (2016). Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems (NeurIPS), 29. link ↗ | Dai, A. M., & Le, Q. V. (2015). Semi-supervised Sequence Learning. Advances in Neural Information Processing Systems (NeurIPS), 28. link ↗ |
| Aliasser | WS-GRU, GRU with weak supervision, weakly labeled GRU, noisy-label GRU | Semi-supervised GRU, SSL-GRU, GRU with unlabeled data, semi-supervised recurrent classifier |
| Relaterede≠ | 6 | 5 |
| Resumé≠ | Weakly Supervised GRU trains a Gated Recurrent Unit network on sequences labeled by imperfect, heuristic, or programmatic sources rather than costly hand-annotated ground truth. It combines the GRU's efficiency at capturing temporal dependencies with weak-supervision techniques that aggregate noisy labels, enabling practical sequence modeling when large fully labeled datasets are unavailable. | Semi-supervised GRU applies the Gated Recurrent Unit architecture to settings where only a small fraction of sequential data is labeled. By first pre-training or jointly training on abundant unlabeled sequences — through language modeling, auto-encoding, or consistency regularization — and then fine-tuning on labeled examples, the model exploits the full corpus to learn richer sequence representations than supervised-only training would allow. |
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