Sammenlign metoder

	Vision Transformer	Generativ modstridende netværk	Random Forest	Support Vector Machine (Klassifikation)
Fagområde	Dyb læring	Dyb læring	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning	Machine learning	Machine learning
Oprindelsesår	2021	2014	2001	1995
Ophavsperson	Dosovitskiy, A. et al.	Goodfellow, I. et al.	Breiman, L.	Cortes, C. & Vapnik, V.
Type	Transformer architecture for images (self-attention over patches)	Generative deep learning (adversarial two-network game)	Ensemble (bagging of decision trees)	Maximum-margin classifier (kernel method)
Oprindelig kilde	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Aliasser	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	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
Relaterede	5	4	4	5
Resumé	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 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.	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.