方法对比
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| 归一化流× | 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≠ | 2015 | 2020 |
| 提出者≠ | Danilo Rezende & Shakir Mohamed | Ho, J., Jain, A. & Abbeel, P. |
| 类型≠ | Generative model via invertible transformations | Generative deep learning (denoising diffusion) |
| 开创性文献≠ | Rezende, D. J., & Mohamed, S. (2015). Variational inference with normalizing flows. International Conference on Machine Learning (ICML), 1530–1538. link ↗ | Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS. link ↗ |
| 别名≠ | Flow-Based Generative Models, Invertible Neural Networks, Exact Likelihood Models, Akışa Dayalı Üretici Modeller | Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM |
| 相关≠ | 2 | 4 |
| 摘要≠ | Normalizing flows are a class of generative models that learn a complex probability distribution by applying a sequence of invertible, differentiable transformations to a simple base distribution such as a standard Gaussian. Introduced by Rezende and Mohamed (2015) in the context of variational inference, they enable exact likelihood computation and efficient sampling, making them a principled alternative to VAEs and GANs for density estimation and generation tasks. | 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. |
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