方法对比
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| 扩散模型× | Neural ODE× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2020 | 2018 |
| 提出者≠ | Ho, J., Jain, A. & Abbeel, P. | Chen, T. Q. et al. |
| 类型≠ | Generative deep learning (denoising diffusion) | Continuous-depth neural network (ODE-parameterised dynamics) |
| 开创性文献≠ | Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS. link ↗ | Chen, T. Q., Rubanova, Y., Bettencourt, J. & Duvenaud, D. (2018). Neural Ordinary Differential Equations. Advances in Neural Information Processing Systems (NeurIPS). link ↗ |
| 别名≠ | Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM | Nöral Diferansiyel Denklem (Neural ODE), neural ordinary differential equation, continuous-depth network, ODE-Net |
| 相关 | 4 | 4 |
| 摘要≠ | A diffusion model is a generative deep-learning method, introduced by Ho, Jain and Abbeel in 2020 (DDPM), that learns to produce high-quality images, audio and molecular structures by reversing a step-by-step noising process. It has largely displaced GANs as the current state of the art in generative modelling. | A Neural ODE, introduced by Chen and colleagues in 2018, models a hidden state as the continuous solution of an ordinary differential equation whose dynamics are parameterised by a neural network. It generalises the limiting case of residual connections, making it well suited to irregularly spaced time series and physics-based modelling. |
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