Compara mètodes

	Variational Autoencoder	Autoencoder	Generative Adversarial Network
Camp	Aprenentatge profund	Aprenentatge profund	Aprenentatge profund
Família	Machine learning	Machine learning	Machine learning
Any d'origen	2014	2006	2014
Autor original	Kingma, D. P. & Welling, M.	Hinton, G.E. & Salakhutdinov, R.R.	Goodfellow, I. et al.
Tipus	Deep generative latent-variable model (encoder–decoder)	Neural network (encoder-decoder)	Generative deep learning (adversarial two-network game)
Font seminal	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
Àlies	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Relacionats	5	4	4
Resum	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.	An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data.	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.