Methoden vergleichen

	Generative Adversarial Network	Random Forest	Vision Transformer
Fachgebiet	Deep Learning	Maschinelles Lernen	Deep Learning
Familie	Machine learning	Machine learning	Machine learning
Entstehungsjahr	2014	2001	2021
Urheber	Goodfellow, I. et al.	Breiman, L.	Dosovitskiy, A. et al.
Typ	Generative deep learning (adversarial two-network game)	Ensemble (bagging of decision trees)	Transformer architecture for images (self-attention over patches)
Wegweisende Quelle	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Aliasnamen	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Verwandt	4	4	5
Zusammenfassung	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 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).