方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 可解释扩散模型× | 自监督扩散模型× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份 | 2020–2022 | 2020–2022 |
| 提出者≠ | Ho, J., Jain, A., & Abbeel, P. (DDPM, 2020); XAI augmentation by subsequent researchers | Ho, J. et al.; extended by Chen, T. et al. and subsequent self-supervised diffusion works |
| 类型≠ | Generative model with post-hoc or intrinsic explainability | Generative model with self-supervised representation objective |
| 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 ↗ | Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851. link ↗ |
| 别名 | XAI-DDPM, interpretable diffusion model, transparent diffusion model, explainable DDPM | SSDM, self-supervised score-based model, diffusion-based self-supervised learning, denoising diffusion with self-supervised pretraining |
| 相关≠ | 6 | 2 |
| 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. | A self-supervised diffusion model couples the iterative noise-and-denoise generative process of denoising diffusion probabilistic models with a self-supervised representation learning objective — such as contrastive or masked prediction loss — so that the model simultaneously learns to generate realistic data and to produce semantically meaningful representations without any labeled examples. |
| ScholarGate数据集 ↗ |
|
|