قارن الطرق

	شبكة الخصومة التوليدية	المعادلة التفاضلية العصبية العادية
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2014	2018
صاحب الطريقة	Goodfellow, I. et al.	Chen, T. Q. et al.
النوع	Generative deep learning (adversarial two-network game)	Continuous-depth neural network (ODE-parameterised dynamics)
المصدر التأسيسي	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).
الأسماء البديلة	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Nöral Diferansiyel Denklem (Neural ODE), neural ordinary differential equation, continuous-depth network, ODE-Net
ذات صلة	4	4
الملخص	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.