قارن الطرق

	المشفر التلقائي التبايني شبه المُشرف	شبكة عصبية التفافية شبه مُشرف عليها
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2014	2013–2017
صاحب الطريقة	Kingma, D. P.; Mohamed, S.; Rezende, D. J.; Wierstra, D.	Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others)
النوع	Generative probabilistic model (semi-supervised)	Semi-supervised deep learning
المصدر التأسيسي	Kingma, D. P., Mohamed, S., Rezende, D. J., & Wierstra, D. (2014). Semi-supervised learning with deep generative models. Advances in Neural Information Processing Systems (NeurIPS), 27, 3581–3589.	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	Semi-supervised VAE, M2 model, VAE with label propagation, deep generative semi-supervised model	SSL-CNN, semi-supervised CNN, self-training CNN, pseudo-label CNN
ذات صلة	6	5
الملخص	The semi-supervised VAE (M2 model) is a deep generative method that jointly learns a latent representation of inputs and a classifier, leveraging both labeled and unlabeled examples in a principled probabilistic framework. Introduced by Kingma et al. in 2014, it allows accurate classification even when labels are scarce by having the generative model explain away unlabeled observations.	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.