Comparer des méthodes

	Modèle de diffusion faiblement supervisé	Autoencodeur Variationnel
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2022–2024	2014
Auteur d'origine	Ho et al. (DDPM foundation); weak supervision integration by multiple groups, 2022–2024	Kingma, D. P. & Welling, M.
Type	Generative model with imperfect supervision	Deep generative latent-variable model (encoder–decoder)
Source fondatrice	Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Alias	WS-Diffusion, weakly supervised DDPM, label-efficient diffusion model, noisy-label diffusion training	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Apparentées	6	5
Résumé	A weakly supervised diffusion model trains or conditions a denoising diffusion probabilistic model using coarse, noisy, or incomplete supervision signals — such as image-level class labels, bounding boxes, or crowd-sourced annotations — instead of pixel-precise ground truth. This allows high-quality generative and discriminative outputs in annotation-scarce settings where full labeling is infeasible or prohibitively expensive.	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.