方法对比
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| 可解释扩散模型× | 可解释生成对抗网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2020–2022 | 2019 (GAN Dissection); ongoing |
| 提出者≠ | Ho, J., Jain, A., & Abbeel, P. (DDPM, 2020); XAI augmentation by subsequent researchers | Bau, D. et al. (GAN Dissection); broader XAI-GAN community |
| 类型≠ | Generative model with post-hoc or intrinsic explainability | Explainable generative model |
| 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 ↗ | Bau, D., Zhu, J.-Y., Strobelt, H., Zhou, B., Tenenbaum, J. B., Freeman, W. T., & Torralba, A. (2019). GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. In Proceedings of the International Conference on Learning Representations (ICLR 2019). link ↗ |
| 别名 | XAI-DDPM, interpretable diffusion model, transparent diffusion model, explainable DDPM | XAI-GAN, Interpretable GAN, Transparent GAN, 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. | Explainable GAN applies interpretability techniques to Generative Adversarial Networks to reveal which internal units and latent directions cause specific visual or structural features in generated outputs. It combines GAN training with post-hoc analysis tools — such as unit dissection, saliency maps, or disentangled latent spaces — to make generative model behaviour transparent and auditable. |
| ScholarGate数据集 ↗ |
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