Võrdle meetodeid

	Nõrgalt juhendatud variatsiooniline autoenkooder	Variational Autoencoder
Valdkond	Süvaõpe	Süvaõpe
Perekond	Machine learning	Machine learning
Tekkeaasta	2014–2018	2014
Looja	Kingma, D. P. et al. (building on VAE and semi-supervised deep generative models)	Kingma, D. P. & Welling, M.
Tüüp	Generative model with weak supervision	Deep generative latent-variable model (encoder–decoder)
Algallikas	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the International Conference on Learning Representations (ICLR 2014).	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Rööpnimetused	WS-VAE, weakly-supervised VAE, semi-supervised VAE with weak labels, label-guided variational autoencoder	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Seotud	3	5
Kokkuvõte	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.	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.