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	Perceptron Đa Lớp Giám Sát Yếu	Mạng perceptron đa lớp bán giám sát
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2016–2018	2006–2013
Người khởi xướng	Multiple contributors; paradigm formalized by Zhou (2018) and Ratner et al. (2016)	Chapelle, O.; Scholkopf, B.; Zien, A. (eds.); Lee, D.-H.
Loại	Feedforward neural network trained under weak supervision	Semi-supervised feedforward neural network
Công trình gốc	Zhou, Z.-H. (2018). A brief introduction to weakly supervised learning. National Science Review, 5(1), 44–53.	Chapelle, O., Scholkopf, B. & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Tên gọi khác	WS-MLP, weakly supervised feedforward network, noisy-label MLP, weak-label multilayer perceptron	SSL-MLP, semi-supervised MLP, semi-supervised feedforward network, partially supervised multilayer perceptron
Liên quan	5	4
Tóm tắt	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.	A semi-supervised multilayer perceptron (SSL-MLP) is a feedforward neural network trained on a small pool of labeled examples together with a larger pool of unlabeled examples. By combining supervised cross-entropy loss on labeled data with an unsupervised consistency or pseudo-label objective on unlabeled data, it extracts far more signal from the data than a purely supervised MLP trained on labels alone.