方法对比

	GPT模型微调	Vision Transformer
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2019	2021
提出者	Radford, A. et al. (OpenAI)	Dosovitskiy, A. et al.
类型	Fine-tuning of pretrained autoregressive language models	Transformer architecture for images (self-attention over patches)
开创性文献	Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Technical Report.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
别名	GPT İnce Ayar ve Talimat Uyarlaması, GPT fine-tuning, instruction tuning, LLM fine-tuning	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
相关	5	5
摘要	GPT fine-tuning adapts pretrained autoregressive language models such as GPT-2/3/4 or LLaMA — introduced in OpenAI's 2019 work by Radford and colleagues — to domain-specific data or to instruction following via reinforcement learning from human feedback (RLHF) or DPO. It is used for instruction following, domain adaptation, and generative tasks.	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).