방법 비교

	Multimodal Variational Autoencoder	전문가 혼합
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2018	2017
창시자	Wu, M. and Goodman, N.	Shazeer, N. et al.
유형	Generative latent-variable model	Sparse neural network architecture (conditional computation)
원전	Wu, M., & Goodman, N. (2018). Multimodal Generative Models for Scalable Weakly-Supervised Learning. Advances in Neural Information Processing Systems (NeurIPS), 31.	Shazeer, N. et al. (2017). Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. ICLR. arXiv:1701.06538
별칭	MVAE, multimodal VAE, multi-modal variational autoencoder, multimodal generative model	Uzman Karışımı (Mixture of Experts — MoE), uzman karışımı, MoE, sparse mixture of experts
관련	3	3
요약	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.	Mixture of Experts (MoE) is a sparse neural-network architecture, introduced by Shazeer and colleagues in 2017 with the sparsely-gated MoE layer, in which only a subset of expert sub-networks is activated for each input. As seen in models such as Switch Transformer and Mixtral, it holds computation cost fixed even as the total parameter count grows.