विधियों की तुलना करें

	जनरेटिव एडवरसैरियल नेटवर्क	डिफ्यूजन मॉडल
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2014	2020
प्रवर्तक	Goodfellow, I. et al.	Ho, J., Jain, A. & Abbeel, P.
प्रकार	Generative deep learning (adversarial two-network game)	Generative deep learning (denoising diffusion)
मौलिक स्रोत	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS.
उपनाम	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM
संबंधित	4	4
सारांश	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 diffusion model is a generative deep-learning method, introduced by Ho, Jain and Abbeel in 2020 (DDPM), that learns to produce high-quality images, audio and molecular structures by reversing a step-by-step noising process. It has largely displaced GANs as the current state of the art in generative modelling.