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| Mô hình Khuếch tán Đa phương thức× | Multimodal Variational Autoencoder× | |
|---|---|---|
| Lĩnh vực | Học sâu | Học sâu |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 2020–2022 | 2018 |
| Người khởi xướng≠ | Ho, J. et al. (DDPM); Rombach, R. et al. (LDM/Stable Diffusion) | Wu, M. and Goodman, N. |
| Loại≠ | Generative model (denoising diffusion) | Generative latent-variable model |
| Công trình gốc≠ | 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. DOI ↗ | Wu, M., & Goodman, N. (2018). Multimodal Generative Models for Scalable Weakly-Supervised Learning. Advances in Neural Information Processing Systems (NeurIPS), 31. link ↗ |
| Tên gọi khác | multimodal DDPM, cross-modal diffusion, conditional multimodal diffusion, multi-modal denoising diffusion | MVAE, multimodal VAE, multi-modal variational autoencoder, multimodal generative model |
| Liên quan≠ | 6 | 3 |
| Tóm tắt≠ | 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. |
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