Comparer des méthodes

	Modèle de diffusion explicable	Modèle de diffusion multimodal
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2020–2022	2020–2022
Auteur d'origine	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)
Type	Generative model with post-hoc or intrinsic explainability	Generative model (denoising diffusion)
Source fondatrice	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.
Alias	XAI-DDPM, interpretable diffusion model, transparent diffusion model, explainable DDPM	multimodal DDPM, cross-modal diffusion, conditional multimodal diffusion, multi-modal denoising diffusion
Apparentées	6	6
Résumé	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.