Σύγκριση μεθόδων

	Ημι-εποπτευόμενο Μοντέλο Διάχυσης	Ημι-επιβλεπόμενη Μάθηση
Πεδίο	Βαθιά Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2020–2022	1970s–2006 (formalized)
Δημιουργός	Multiple groups (Ho et al., Song et al., and successors)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Τύπος	Generative model with semi-supervised guidance	Learning paradigm
Θεμελιώδης πηγή	Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., & Ganguli, S. (2015). Deep Unsupervised Learning using Nonequilibrium Thermodynamics. Proceedings of the 32nd International Conference on Machine Learning (ICML), 2256–2265.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Εναλλακτικές ονομασίες	Semi-supervised DDPM, Label-guided diffusion model, Semi-supervised score-based generative model, SSL diffusion	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Συναφείς	3	5
Σύνοψη	A semi-supervised diffusion model extends the denoising diffusion probabilistic framework to settings where only a fraction of training samples carry class labels. By combining an unconditional diffusion backbone with a lightweight classifier trained on labeled examples, it learns to generate high-quality, label-conditioned outputs while still exploiting the structure in unlabeled data.	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.