So sánh phương pháp

	Multimodal Variational Autoencoder	Generative Adversarial Network
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2018	2014
Người khởi xướng	Wu, M. and Goodman, N.	Goodfellow, I. et al.
Loại	Generative latent-variable model	Generative deep learning (adversarial two-network game)
Công trình gốc	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.
Tên gọi khác	MVAE, multimodal VAE, multi-modal variational autoencoder, multimodal generative model	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Liên quan	3	4
Tóm tắt	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.