Comparer des méthodes

	Autoencodeur variationnel auto-supervisé	Réseau antagoniste génératif
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2014 (VAE); self-supervised variant ~2019–2021	2014
Auteur d'origine	Kingma, D. P. & Welling, M. (VAE); self-supervised extensions by various authors from ~2019 onward	Goodfellow, I. et al.
Type	Generative model with self-supervised representation learning	Generative deep learning (adversarial two-network game)
Source fondatrice	Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
Alias	SS-VAE, self-supervised VAE, unsupervised VAE with self-supervised pretext tasks, contrastive VAE	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Apparentées	6	4
Résumé	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.	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.