Bandingkan kaedah

	Semi-supervised Multilayer Perceptron	Multilayer Perceptron yang Ditalar Halus
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2006–2013	1986 (MLP); fine-tuning practice formalised c. 2014
Pengasas	Chapelle, O.; Scholkopf, B.; Zien, A. (eds.); Lee, D.-H.	Rumelhart, Hinton & Williams (MLP); Yosinski et al. (fine-tuning analysis)
Jenis	Semi-supervised feedforward neural network	Supervised deep learning with pre-trained weight initialisation
Sumber perintis	Chapelle, O., Scholkopf, B. & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.
Alias	SSL-MLP, semi-supervised MLP, semi-supervised feedforward network, partially supervised multilayer perceptron	fine-tuned MLP, adapted MLP, domain-adapted multilayer perceptron, MLP fine-tuning
Berkaitan	4	4
Ringkasan	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 Fine-Tuned Multilayer Perceptron starts from weights learned on a source task — or a large general-purpose dataset — and continues training on a smaller target dataset with a reduced learning rate. This reuse of pre-learned representations allows the MLP to converge faster and generalise better than training from scratch, especially when labelled target data is scarce.