Сравнение методов

	Объяснимая диффузионная модель	Мультимодальная диффузионная модель
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2020–2022	2020–2022
Автор метода	Ho, J., Jain, A., & Abbeel, P. (DDPM, 2020); XAI augmentation by subsequent researchers	Ho, J. et al. (DDPM); Rombach, R. et al. (LDM/Stable Diffusion)
Тип	Generative model with post-hoc or intrinsic explainability	Generative model (denoising diffusion)
Основополагающий источник	Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems, 33, 6840–6851.	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.
Другие названия	XAI-DDPM, interpretable diffusion model, transparent diffusion model, explainable DDPM	multimodal DDPM, cross-modal diffusion, conditional multimodal diffusion, multi-modal denoising diffusion
Связанные	6	6
Сводка	An Explainable Diffusion Model couples a denoising diffusion probabilistic model with post-hoc or intrinsic explainability techniques — such as SHAP, gradient-based saliency, attention analysis, or concept-based probing — so that each generative or predictive decision can be audited and justified rather than treated as a black box.	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.