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	マルチモーダル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	2018	2014
提唱者	Wu, M. and Goodman, N.	Goodfellow, I. et al.
種類	Generative latent-variable model	Generative deep learning (adversarial two-network game)
原典	Wu, M., & Goodman, N. (2018). Multimodal Generative Models for Scalable Weakly-Supervised Learning. Advances in Neural Information Processing Systems (NeurIPS), 31.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
別名	MVAE, multimodal VAE, multi-modal variational autoencoder, multimodal generative model	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
関連	3	4
概要	The Multimodal Variational Autoencoder (MVAE) is a deep generative model that learns a shared latent representation across two or more data modalities — such as images and captions — using a product-of-experts fusion of modality-specific encoders, enabling generation and inference even when only a subset of modalities is observed at test time.	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.