قارن الطرق

	المشفر التلقائي التبايني متعدد الوسائط	شبكة الخصومة التوليدية
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2018	2014
صاحب الطريقة	Wu, M. and Goodman, N.	Goodfellow, I. et al.
النوع	Generative latent-variable model	Generative deep learning (adversarial two-network game)
المصدر التأسيسي	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.
الأسماء البديلة	MVAE, multimodal VAE, multi-modal variational autoencoder, multimodal generative model	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
ذات صلة	3	4
الملخص	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.