Сравнение методов

	Генеративно-состязательная сеть	Vision Transformer
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2014	2021
Автор метода	Goodfellow, I. et al.	Dosovitskiy, A. et al.
Тип	Generative deep learning (adversarial two-network game)	Transformer architecture for images (self-attention over patches)
Основополагающий источник	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Другие названия	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Связанные	4	5
Сводка	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 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).