Σύγκριση μεθόδων

	Μοντέλο Διάχυσης Ασθενώς Επιβλεπόμενο	Variational Autoencoder
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2022–2024	2014
Δημιουργός	Ho et al. (DDPM foundation); weak supervision integration by multiple groups, 2022–2024	Kingma, D. P. & Welling, M.
Τύπος	Generative model with imperfect supervision	Deep generative latent-variable model (encoder–decoder)
Θεμελιώδης πηγή	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).
Εναλλακτικές ονομασίες	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
Συναφείς	6	5
Σύνοψη	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.