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可解释扩散模型

可解释扩散模型将去噪扩散概率模型与事后或内在可解释性技术相结合——例如 SHAP、基于梯度的显著性、注意力分析或基于概念的探测——从而使每个生成或预测决策都可以被审计和证明，而不是被视为黑箱。

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可解释扩散模型
可解释生成对抗网络可解释变分自编码器可解释视觉 Transformer微调扩散模型多模态扩散模型自监督扩散模型

来源

  1. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems, 33, 6840–6851. link ↗
  2. Diffusion model. Wikipedia. link ↗

如何引用本页

ScholarGate. (2026, June 3). Explainable Diffusion Model (XAI-Augmented Denoising Diffusion Probabilistic Model). ScholarGate. https://scholargate.app/zh/deep-learning/explainable-diffusion-model

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

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ScholarGate — Explainable Diffusion Model (Explainable Diffusion Model (XAI-Augmented Denoising Diffusion Probabilistic Model)). 于 2026-06-15 检索自 https://scholargate.app/zh/deep-learning/explainable-diffusion-model · 数据集： https://doi.org/10.5281/zenodo.20539026