পদ্ধতির তুলনা করুন

	মাল্টিমোডাল ভ্যারিয়েশনাল অটোএনকোডার	বিশেষজ্ঞদের মিশ্রণ
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2018	2017
প্রবর্তক	Wu, M. and Goodman, N.	Shazeer, N. et al.
ধরন	Generative latent-variable model	Sparse neural network architecture (conditional computation)
মৌলিক উৎস	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
অপর নাম	MVAE, multimodal VAE, multi-modal variational autoencoder, multimodal generative model	Uzman Karışımı (Mixture of Experts — MoE), uzman karışımı, MoE, sparse mixture of experts
সম্পর্কিত	3	3
সারসংক্ষেপ	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.