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	Generative Adversarial Network	スコアベース生成モデル
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2014	2019
提唱者	Goodfellow, I. et al.	Song, Y. & Ermon, S.
種類	Generative deep learning (adversarial two-network game)	Score-based generative model (SDE framework)
原典	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.
別名	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
関連	4	5
概要	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 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.