方法对比

	生成对抗网络	Vision Transformer
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2014	2021
提出者	Goodfellow, I. et al.	Dosovitskiy, A. et al.
类型	Generative deep learning (adversarial two-network game)	Transformer architecture for images (self-attention over patches)
开创性文献	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.
别名	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
相关	4	5
摘要	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).