Krahasoni metodat

	Modeli i Difuzionit Gjysmë i Mbikëqyrur	Mësimi Gjysmë i Mbikëqyrur
Fusha	Mësimi i thellë	Mësimi i makinës
Familja	Machine learning	Machine learning
Viti i origjinës	2020–2022	1970s–2006 (formalized)
Krijuesi	Multiple groups (Ho et al., Song et al., and successors)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Lloji	Generative model with semi-supervised guidance	Learning paradigm
Burimi themelues	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
Emërtime të tjera	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
Të lidhura	3	5
Përmbledhja	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.