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	Autoencoder Variazionale Debolmente Supervisionato	Rete Generativa Avversaria
Campo	Apprendimento profondo	Apprendimento profondo
Famiglia	Machine learning	Machine learning
Anno di origine	2014–2018	2014
Ideatore	Kingma, D. P. et al. (building on VAE and semi-supervised deep generative models)	Goodfellow, I. et al.
Tipo	Generative model with weak supervision	Generative deep learning (adversarial two-network game)
Fonte seminale	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the International Conference on Learning Representations (ICLR 2014).	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
Alias	WS-VAE, weakly-supervised VAE, semi-supervised VAE with weak labels, label-guided variational autoencoder	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Correlati	3	4
Sintesi	A Weakly Supervised Variational Autoencoder (WS-VAE) extends the standard VAE generative framework by incorporating partial, noisy, or coarse supervision signals — such as crowd-sourced labels, heuristic rules, or programmatic annotations — to guide latent space learning without requiring fully annotated data. It is widely applied in computer vision, NLP, and biomedical domains where complete ground-truth labels are expensive or unavailable.	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.