방법 비교

	Vision Transformer	확산 모델
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2021	2020
창시자	Dosovitskiy, A. et al.	Ho, J., Jain, A. & Abbeel, P.
유형	Transformer architecture for images (self-attention over patches)	Generative deep learning (denoising diffusion)
원전	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.
별칭	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images	Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM
관련	5	4
요약	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.