विधियों की तुलना करें

	वैरिएशन ऑटोएन्कोडर	डिफ्यूजन मॉडल	प्रिंसिपल कंपोनेंट एनालिसिस
क्षेत्र	गहन अधिगम	गहन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning	Machine learning
उद्भव वर्ष	2014	2020	2002
प्रवर्तक	Kingma, D. P. & Welling, M.	Ho, J., Jain, A. & Abbeel, P.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
प्रकार	Deep generative latent-variable model (encoder–decoder)	Generative deep learning (denoising diffusion)	Unsupervised dimensionality reduction
मौलिक स्रोत	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.
उपनाम	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
संबंधित	5	4	3
सारांश	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.