方法证据记录
Weakly supervised transformer
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Weakly Supervised Transformer
分类方法记录 · ml-model / deep-learning
- 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 10.14778/3157794.3157797
- Zhou, Z.-H. (2018). A brief introduction to weakly supervised learning. National Science Review, 5(1), 44–53. · DOI 10.1093/nsr/nwx106
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