Compară metode

	Autoencoder Variațional	Rețea Generativă Adversarial	Analiza Componentelor Principale
Domeniu	Învățare profundă	Învățare profundă	Învățare automată
Familie	Machine learning	Machine learning	Machine learning
Anul apariției	2014	2014	2002
Autorul original	Kingma, D. P. & Welling, M.	Goodfellow, I. et al.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Tip	Deep generative latent-variable model (encoder–decoder)	Generative deep learning (adversarial two-network game)	Unsupervised dimensionality reduction
Sursa seminală	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Denumiri alternative	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Înrudite	5	4	3
Rezumat	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 Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation.	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.