Võrdle meetodeid

	Vision Transformer	Difusioonimudel	Support Vector Machine (Klassifitseerimine)
Valdkond	Süvaõpe	Süvaõpe	Masinõpe
Perekond	Machine learning	Machine learning	Machine learning
Tekkeaasta	2021	2020	1995
Looja	Dosovitskiy, A. et al.	Ho, J., Jain, A. & Abbeel, P.	Cortes, C. & Vapnik, V.
Tüüp	Transformer architecture for images (self-attention over patches)	Generative deep learning (denoising diffusion)	Maximum-margin classifier (kernel method)
Algallikas	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.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Rööpnimetused	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images	Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Seotud	5	4	5
Kokkuvõte	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.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.