Salīdzināt metodes

	Generatīvais modelis, kas balstīts uz skaitlisko vērtību (rezultātu)	Variacionālais autoenkoders
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2019	2014
Autors	Song, Y. & Ermon, S.	Kingma, D. P. & Welling, M.
Tips	Score-based generative model (SDE framework)	Deep generative latent-variable model (encoder–decoder)
Pirmavots	Song, Y. & Ermon, S. (2019). Generative Modeling by Estimating Gradients of the Data Distribution. NeurIPS 32, 11895–11907.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Citi nosaukumi	Skor Tabanlı Üretici Model (Score-Based / SDE), score-based diffusion, SDE-based generative model, score SDE	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Saistītās	5	5
Kopsavilkums	A score-based generative model, introduced by Yang Song and Stefano Ermon in 2019 and generalized to the stochastic differential equation (SDE) framework in 2021, learns the gradient of the data density — the score — rather than predicting noise directly, and uses it to generate new samples. It is the mathematical generalization that unifies diffusion models under a continuous-time formulation.	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.