Сравнение на методи

	Дифузионен модел	Невронно ОДУ
Област	Дълбоко обучение	Дълбоко обучение
Семейство	Machine learning	Machine learning
Година на възникване	2020	2018
Създател	Ho, J., Jain, A. & Abbeel, P.	Chen, T. Q. et al.
Тип	Generative deep learning (denoising diffusion)	Continuous-depth neural network (ODE-parameterised dynamics)
Основополагащ източник	Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS.	Chen, T. Q., Rubanova, Y., Bettencourt, J. & Duvenaud, D. (2018). Neural Ordinary Differential Equations. Advances in Neural Information Processing Systems (NeurIPS).
Други названия	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
Свързани	4	4
Резюме	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.