方法对比

	生成对抗网络	Neural ODE
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2014	2018
提出者	Goodfellow, I. et al.	Chen, T. Q. et al.
类型	Generative deep learning (adversarial two-network game)	Continuous-depth neural network (ODE-parameterised dynamics)
开创性文献	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Chen, T. Q., Rubanova, Y., Bettencourt, J. & Duvenaud, D. (2018). Neural Ordinary Differential Equations. Advances in Neural Information Processing Systems (NeurIPS).
别名	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Nöral Diferansiyel Denklem (Neural ODE), neural ordinary differential equation, continuous-depth network, ODE-Net
相关	4	4
摘要	A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation.	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.