Сравнение методов

	Тонкая настройка GPT	Случайный лес	Vision Transformer
Область	Глубокое обучение	Машинное обучение	Глубокое обучение
Семейство	Machine learning	Machine learning	Machine learning
Год появления	2019	2001	2021
Автор метода	Radford, A. et al. (OpenAI)	Breiman, L.	Dosovitskiy, A. et al.
Тип	Fine-tuning of pretrained autoregressive language models	Ensemble (bagging of decision trees)	Transformer architecture for images (self-attention over patches)
Основополагающий источник	Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Technical Report.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Другие названия	GPT İnce Ayar ve Talimat Uyarlaması, GPT fine-tuning, instruction tuning, LLM fine-tuning	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Связанные	5	4	5
Сводка	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	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).