Comparar métodos

	Autoencoder Variacional Auto-supervisado	Autoencoder Variacional
Campo	Aprendizaje profundo	Aprendizaje profundo
Familia	Machine learning	Machine learning
Año de origen	2014 (VAE); self-supervised variant ~2019–2021	2014
Autor original	Kingma, D. P. & Welling, M. (VAE); self-supervised extensions by various authors from ~2019 onward	Kingma, D. P. & Welling, M.
Tipo	Generative model with self-supervised representation learning	Deep generative latent-variable model (encoder–decoder)
Fuente seminal	Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Alias	SS-VAE, self-supervised VAE, unsupervised VAE with self-supervised pretext tasks, contrastive VAE	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Relacionados	6	5
Resumen	A Self-supervised Variational Autoencoder (SS-VAE) combines the generative latent-space learning of a standard VAE with self-supervised pretext tasks — such as contrastive augmentation, masked reconstruction, or rotation prediction — to learn richer, more disentangled representations from unlabeled data without any manual annotation.	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.