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	Variational Autoencoder	Modello di diffusione	Analisi delle Componenti Principali
Campo	Apprendimento profondo	Apprendimento profondo	Apprendimento automatico
Famiglia	Machine learning	Machine learning	Machine learning
Anno di origine	2014	2020	2002
Ideatore	Kingma, D. P. & Welling, M.	Ho, J., Jain, A. & Abbeel, P.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Tipo	Deep generative latent-variable model (encoder–decoder)	Generative deep learning (denoising diffusion)	Unsupervised dimensionality reduction
Fonte seminale	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).	Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Alias	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model	Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Correlati	5	4	3
Sintesi	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.	A diffusion model is a generative deep-learning method, introduced by Ho, Jain and Abbeel in 2020 (DDPM), that learns to produce high-quality images, audio and molecular structures by reversing a step-by-step noising process. It has largely displaced GANs as the current state of the art in generative modelling.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.