Salīdzināt metodes

	Generatīvais Adversariālais Tīkls	Variacionālais autoenkoders
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2014	2014
Autors	Goodfellow, I. et al.	Kingma, D. P. & Welling, M.
Tips	Generative deep learning (adversarial two-network game)	Deep generative latent-variable model (encoder–decoder)
Pirmavots	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Citi nosaukumi	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Saistītās	4	5
Kopsavilkums	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.	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.