Compara mètodes

	Vision Transformer	Generative Adversarial Network	Màquina de Vectors de Suport (Classificació)
Camp	Aprenentatge profund	Aprenentatge profund	Aprenentatge automàtic
Família	Machine learning	Machine learning	Machine learning
Any d'origen	2021	2014	1995
Autor original	Dosovitskiy, A. et al.	Goodfellow, I. et al.	Cortes, C. & Vapnik, V.
Tipus	Transformer architecture for images (self-attention over patches)	Generative deep learning (adversarial two-network game)	Maximum-margin classifier (kernel method)
Font seminal	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.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Àlies	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Relacionats	5	4	5
Resum	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.	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.