Vertaile menetelmiä

	Vision Transformer	Diffuusiomalli	Random Forest	Tukivektorikone (luokittelu)
Tieteenala	Syväoppiminen	Syväoppiminen	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning	Machine learning	Machine learning
Syntyvuosi	2021	2020	2001	1995
Kehittäjä	Dosovitskiy, A. et al.	Ho, J., Jain, A. & Abbeel, P.	Breiman, L.	Cortes, C. & Vapnik, V.
Tyyppi	Transformer architecture for images (self-attention over patches)	Generative deep learning (denoising diffusion)	Ensemble (bagging of decision trees)	Maximum-margin classifier (kernel method)
Alkuperäislähde	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Rinnakkaisnimet	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images	Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Liittyvät	5	4	4	5
Tiivistelmä	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	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.