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	ドメイン適応型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	2020	2014
提唱者	Ilse, M.; Tomczak, J. M.; Louizos, C.; Welling, M.	Goodfellow, I. et al.
種類	Generative model with domain adaptation	Generative deep learning (adversarial two-network game)
原典	Ilse, M., Tomczak, J. M., Louizos, C., & Welling, M. (2020). DIVA: Domain Invariant Variational Autoencoders. Proceedings of the Third Conference on Medical Imaging with Deep Learning (MIDL 2020), PMLR 121, 322–348.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
別名	DA-VAE, domain-adaptive VAE, domain-conditioned variational autoencoder, cross-domain VAE	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
関連	3	4
概要	A Domain-Adaptive Variational Autoencoder (DA-VAE) extends the standard VAE framework to learn disentangled latent representations that separate domain-specific variation from class-relevant and domain-invariant content, enabling models trained on a source domain to generalise effectively to a different but related target domain with limited or no target labels.	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.