Comparar métodos

	Autoencoder Variacional Semi-supervisado	Autoencoder Variacional
Campo	Aprendizaje profundo	Aprendizaje profundo
Familia	Machine learning	Machine learning
Año de origen	2014	2014
Autor original	Kingma, D. P.; Mohamed, S.; Rezende, D. J.; Wierstra, D.	Kingma, D. P. & Welling, M.
Tipo	Generative probabilistic model (semi-supervised)	Deep generative latent-variable model (encoder–decoder)
Fuente seminal	Kingma, D. P., Mohamed, S., Rezende, D. J., & Wierstra, D. (2014). Semi-supervised learning with deep generative models. Advances in Neural Information Processing Systems (NeurIPS), 27, 3581–3589.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Alias	Semi-supervised VAE, M2 model, VAE with label propagation, deep generative semi-supervised model	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Relacionados	6	5
Resumen	The semi-supervised VAE (M2 model) is a deep generative method that jointly learns a latent representation of inputs and a classifier, leveraging both labeled and unlabeled examples in a principled probabilistic framework. Introduced by Kingma et al. in 2014, it allows accurate classification even when labels are scarce by having the generative model explain away unlabeled observations.	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.