Salīdzināt metodes

	Multimodāls difūzijas modelis	Daudzmodāls variāciju autoenkoders
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2020–2022	2018
Autors	Ho, J. et al. (DDPM); Rombach, R. et al. (LDM/Stable Diffusion)	Wu, M. and Goodman, N.
Tips	Generative model (denoising diffusion)	Generative latent-variable model
Pirmavots	Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 10684–10695.	Wu, M., & Goodman, N. (2018). Multimodal Generative Models for Scalable Weakly-Supervised Learning. Advances in Neural Information Processing Systems (NeurIPS), 31.
Citi nosaukumi	multimodal DDPM, cross-modal diffusion, conditional multimodal diffusion, multi-modal denoising diffusion	MVAE, multimodal VAE, multi-modal variational autoencoder, multimodal generative model
Saistītās	6	3
Kopsavilkums	A multimodal diffusion model extends denoising diffusion probabilistic models to generate or understand content by conditioning on signals from multiple modalities — such as text, image, audio, or video — simultaneously. It learns to reverse a noise process guided by cross-modal context, enabling high-fidelity synthesis and translation across modalities.	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.