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	自己教師あり変分オートエンコーダ	Generative Adversarial Network
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2014 (VAE); self-supervised variant ~2019–2021	2014
提唱者	Kingma, D. P. & Welling, M. (VAE); self-supervised extensions by various authors from ~2019 onward	Goodfellow, I. et al.
種類	Generative model with self-supervised representation learning	Generative deep learning (adversarial two-network game)
原典	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.
別名	SS-VAE, self-supervised VAE, unsupervised VAE with self-supervised pretext tasks, contrastive VAE	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
関連	6	4
概要	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.