Linganisha mbinu

	Multilayer Perceptron yenye usimamizi nusu	Mtandao wa Mawasiliano wa Nusu-Usindikaji
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2006–2013	2013–2017
Mwanzilishi	Chapelle, O.; Scholkopf, B.; Zien, A. (eds.); Lee, D.-H.	Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others)
Aina	Semi-supervised feedforward neural network	Semi-supervised deep learning
Chanzo asilia	Chapelle, O., Scholkopf, B. & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Lee, D.-H. (2013). Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. ICML Workshop on Challenges in Representation Learning.
Majina mbadala	SSL-MLP, semi-supervised MLP, semi-supervised feedforward network, partially supervised multilayer perceptron	SSL-CNN, semi-supervised CNN, self-training CNN, pseudo-label CNN
Zinazohusiana	4	5
Muhtasari	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 Semi-supervised CNN trains a convolutional network on a small labeled image set and a larger pool of unlabeled images simultaneously, using techniques such as pseudo-labeling and consistency regularization to extract supervisory signal from unlabeled data. This strategy closes much of the performance gap caused by scarce annotations without requiring additional human labeling effort.