Sammenlign metoder

	Svagt Overvåget Multilayer Perceptron	Svagt superviseret Transformer
Fagområde	Dyb læring	Dyb læring
Familie	Machine learning	Machine learning
Oprindelsesår	2016–2018	2017–2019
Ophavsperson	Multiple contributors; paradigm formalized by Zhou (2018) and Ratner et al. (2016)	Multiple contributors (weak supervision paradigm: Zhou 2018; transformer backbone: Vaswani et al. 2017)
Type	Feedforward neural network trained under weak supervision	Weakly supervised deep learning
Oprindelig kilde	Zhou, Z.-H. (2018). A brief introduction to weakly supervised learning. National Science Review, 5(1), 44–53.	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.
Aliasser	WS-MLP, weakly supervised feedforward network, noisy-label MLP, weak-label multilayer perceptron	WST, weakly supervised attention model, noisy-label transformer, weak supervision with transformers
Relaterede	5	5
Resumé	A Weakly Supervised Multilayer Perceptron trains a standard feedforward neural network when only imperfect supervision is available — labels may be noisy, incomplete, crowd-sourced, rule-generated, or derived from distant supervision — enabling learning at scale without the cost of full expert 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.