Compară metode

	Model de difuzie semisupervizat	Autoencoder Variațional
Domeniu	Învățare profundă	Învățare profundă
Familie	Machine learning	Machine learning
Anul apariției	2020–2022	2014
Autorul original	Multiple groups (Ho et al., Song et al., and successors)	Kingma, D. P. & Welling, M.
Tip	Generative model with semi-supervised guidance	Deep generative latent-variable model (encoder–decoder)
Sursa seminală	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.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Denumiri alternative	Semi-supervised DDPM, Label-guided diffusion model, Semi-supervised score-based generative model, SSL diffusion	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Înrudite	3	5
Rezumat	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.	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.