So sánh phương pháp

	Mạng perceptron đa lớp bán giám sát	Perceptron Đa Lớp Giám Sát Yếu
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2006–2013	2016–2018
Người khởi xướng	Chapelle, O.; Scholkopf, B.; Zien, A. (eds.); Lee, D.-H.	Multiple contributors; paradigm formalized by Zhou (2018) and Ratner et al. (2016)
Loại	Semi-supervised feedforward neural network	Feedforward neural network trained under weak supervision
Công trình gốc	Chapelle, O., Scholkopf, B. & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Zhou, Z.-H. (2018). A brief introduction to weakly supervised learning. National Science Review, 5(1), 44–53.
Tên gọi khác	SSL-MLP, semi-supervised MLP, semi-supervised feedforward network, partially supervised multilayer perceptron	WS-MLP, weakly supervised feedforward network, noisy-label MLP, weak-label multilayer perceptron
Liên quan	4	5
Tóm tắt	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.	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.