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| 설명 가능한 확산 모델× | 설명 가능한 비전 트랜스포머(Explainable Vision Transformer)× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2020–2022 | 2021 |
| 창시자≠ | Ho, J., Jain, A., & Abbeel, P. (DDPM, 2020); XAI augmentation by subsequent researchers | Chefer, H., Gur, S., & Wolf, L. (attribution framework); Dosovitskiy et al. (base ViT) |
| 유형≠ | Generative model with post-hoc or intrinsic explainability | Post-hoc explainability applied to Vision Transformer |
| 원전≠ | Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems, 33, 6840–6851. link ↗ | Chefer, H., Gur, S., & Wolf, L. (2021). Transformer interpretability beyond attention visualization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 782–791. DOI ↗ |
| 별칭 | XAI-DDPM, interpretable diffusion model, transparent diffusion model, explainable DDPM | XViT, Interpretable ViT, Explainable ViT, Transparent Vision Transformer |
| 관련≠ | 6 | 5 |
| 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. | Explainable Vision Transformer combines the strong image-recognition performance of Vision Transformers (ViT) with attribution techniques — such as relevance propagation, attention rollout, or gradient-weighted attention — that highlight which image regions drive each prediction. The approach enables researchers and practitioners to audit model decisions and satisfy transparency requirements without sacrificing accuracy. |
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