পদ্ধতির তুলনা করুন

	আধা-তত্ত্বাবধানাধীন শিখন (Semi-supervised Learning)	ভেরিয়েশনাল অটোএনকোডার
ক্ষেত্র	যন্ত্র শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1970s–2006 (formalized)	2014
প্রবর্তক	Vapnik, V. N. and others (community of researchers, 1970s–2000s)	Kingma, D. P. & Welling, M.
ধরন	Learning paradigm	Deep generative latent-variable model (encoder–decoder)
মৌলিক উৎস	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
অপর নাম	SSL, semi-supervised machine learning, transductive learning, label-efficient learning	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
সম্পর্কিত	5	5
সারসংক্ষেপ	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.	The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning.