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| 설명 가능한 확산 모델× | 다중 양식 확산 모델× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도 | 2020–2022 | 2020–2022 |
| 창시자≠ | 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) |
| 유형≠ | Generative model with post-hoc or intrinsic explainability | Generative model (denoising diffusion) |
| 원전≠ | Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems, 33, 6840–6851. link ↗ | 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 ↗ |
| 별칭 | XAI-DDPM, interpretable diffusion model, transparent diffusion model, explainable DDPM | multimodal DDPM, cross-modal diffusion, conditional multimodal diffusion, multi-modal denoising diffusion |
| 관련 | 6 | 6 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
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