방법 비교

	Vision Transformer	생성적 적대 신경망
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2021	2014
창시자	Dosovitskiy, A. et al.	Goodfellow, I. et al.
유형	Transformer architecture for images (self-attention over patches)	Generative deep learning (adversarial two-network game)
원전	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.
별칭	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
관련	5	4
요약	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.