Σύγκριση μεθόδων

	Vision Transformer	Μοντέλο Διάχυσης	Γενετικό Ανταγωνιστικό Δίκτυο	Μηχανή Υποστήριξης Διανυσμάτων (Ταξινόμηση)
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση	Βαθιά Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning	Machine learning	Machine learning
Έτος προέλευσης	2021	2020	2014	1995
Δημιουργός	Dosovitskiy, A. et al.	Ho, J., Jain, A. & Abbeel, P.	Goodfellow, I. et al.	Cortes, C. & Vapnik, V.
Τύπος	Transformer architecture for images (self-attention over patches)	Generative deep learning (denoising diffusion)	Generative deep learning (adversarial two-network game)	Maximum-margin classifier (kernel method)
Θεμελιώδης πηγή	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.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Εναλλακτικές ονομασίες	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images	Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Συναφείς	5	4	4	5
Σύνοψη	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.	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.