方法对比
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| 可解释扩散模型× | 可解释变分自编码器× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2020–2022 | 2013–2017 |
| 提出者≠ | Ho, J., Jain, A., & Abbeel, P. (DDPM, 2020); XAI augmentation by subsequent researchers | Kingma, D. P. & Welling, M. (VAE); Higgins et al. (beta-VAE for disentanglement) |
| 类型≠ | Generative model with post-hoc or intrinsic explainability | Generative model with interpretable latent space |
| 开创性文献≠ | Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems, 33, 6840–6851. link ↗ | Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014). link ↗ |
| 别名 | XAI-DDPM, interpretable diffusion model, transparent diffusion model, explainable DDPM | XVAE, Interpretable VAE, Disentangled Variational Autoencoder, Explainable Generative Model |
| 相关≠ | 6 | 4 |
| 摘要≠ | 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. | An Explainable Variational Autoencoder (XVAE) extends the standard VAE framework with techniques that make its latent space interpretable: disentangling latent dimensions so each corresponds to a human-understandable factor, or post-hoc attribution methods (SHAP, integrated gradients) that trace reconstructions back to input features. It retains the VAE's generative power while adding transparency required in scientific and high-stakes applications. |
| ScholarGate数据集 ↗ |
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