قارن الطرق

	الشبكة العصبية متعددة الطبقات شبه المُشْرَف عليها	شبكة عصبية التفافية شبه مُشرف عليها
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2006–2013	2013–2017
صاحب الطريقة	Chapelle, O.; Scholkopf, B.; Zien, A. (eds.); Lee, D.-H.	Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others)
النوع	Semi-supervised feedforward neural network	Semi-supervised deep learning
المصدر التأسيسي	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.
الأسماء البديلة	SSL-MLP, semi-supervised MLP, semi-supervised feedforward network, partially supervised multilayer perceptron	SSL-CNN, semi-supervised CNN, self-training CNN, pseudo-label CNN
ذات صلة	4	5
الملخص	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.