পদ্ধতির তুলনা করুন

	জেনারেটিভ অ্যাডভারসারিয়াল নেটওয়ার্ক	স্কোর-ভিত্তিক জেনারেটিভ মডেল
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2014	2019
প্রবর্তক	Goodfellow, I. et al.	Song, Y. & Ermon, S.
ধরন	Generative deep learning (adversarial two-network game)	Score-based generative model (SDE framework)
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	4	5
সারসংক্ষেপ	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.