قارن الطرق

	شبكة الخصومة التوليدية	النموذج التوليدي القائم على النقاط
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2014	2019
صاحب الطريقة	Goodfellow, I. et al.	Song, Y. & Ermon, S.
النوع	Generative deep learning (adversarial two-network game)	Score-based generative model (SDE framework)
المصدر التأسيسي	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Song, Y. & Ermon, S. (2019). Generative Modeling by Estimating Gradients of the Data Distribution. NeurIPS 32, 11895–11907.
الأسماء البديلة	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Skor Tabanlı Üretici Model (Score-Based / SDE), score-based diffusion, SDE-based generative model, score SDE
ذات صلة	4	5
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 score-based generative model, introduced by Yang Song and Stefano Ermon in 2019 and generalized to the stochastic differential equation (SDE) framework in 2021, learns the gradient of the data density — the score — rather than predicting noise directly, and uses it to generate new samples. It is the mathematical generalization that unifies diffusion models under a continuous-time formulation.