手法を比較

	マルチモーダルGAN	マルチモーダル変分オートエンコーダ
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2014–2016	2018
提唱者	Reed et al. (text-to-image GAN); foundation by Goodfellow et al.	Wu, M. and Goodman, N.
種類	Generative adversarial model	Generative latent-variable model
原典	Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016). Generative adversarial text to image synthesis. Proceedings of the 33rd International Conference on Machine Learning (ICML), PMLR 48, 1060–1069.	Wu, M., & Goodman, N. (2018). Multimodal Generative Models for Scalable Weakly-Supervised Learning. Advances in Neural Information Processing Systems (NeurIPS), 31.
別名	MM-GAN, multimodal generative adversarial network, cross-modal GAN, multi-modal GAN	MVAE, multimodal VAE, multi-modal variational autoencoder, multimodal generative model
関連	4	3
概要	A Multimodal GAN is a generative adversarial network conditioned on — or jointly learning across — more than one data modality (e.g., text descriptions, images, audio, or structured data). By fusing information from multiple sources, the generator can synthesize realistic outputs that respect cross-modal constraints, enabling tasks such as text-to-image synthesis, image-to-audio generation, and joint modality imputation.	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.