পদ্ধতির তুলনা করুন

	জেনারেটিভ অ্যাডভারসারিয়াল নেটওয়ার্ক	ডিফিউশন মডেল
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2014	2020
প্রবর্তক	Goodfellow, I. et al.	Ho, J., Jain, A. & Abbeel, P.
ধরন	Generative deep learning (adversarial two-network game)	Generative deep learning (denoising diffusion)
মৌলিক উৎস	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS.
অপর নাম	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM
সম্পর্কিত	4	4
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 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.