Сравнение методов

	Vision Transformer	Генеративно-состязательная сеть
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2021	2014
Автор метода	Dosovitskiy, A. et al.	Goodfellow, I. et al.
Тип	Transformer architecture for images (self-attention over patches)	Generative deep learning (adversarial two-network game)
Основополагающий источник	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.
Другие названия	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Связанные	5	4
Сводка	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.