Methoden vergleichen

	Vision Transformer	Diffusion Model	Generative Adversarial Network
Fachgebiet	Deep Learning	Deep Learning	Deep Learning
Familie	Machine learning	Machine learning	Machine learning
Entstehungsjahr	2021	2020	2014
Urheber	Dosovitskiy, A. et al.	Ho, J., Jain, A. & Abbeel, P.	Goodfellow, I. et al.
Typ	Transformer architecture for images (self-attention over patches)	Generative deep learning (denoising diffusion)	Generative deep learning (adversarial two-network game)
Wegweisende Quelle	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.	Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
Aliasnamen	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images	Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Verwandt	5	4	4
Zusammenfassung	The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs).	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.	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.