Порівняння методів

	Доналаштування BERT	LoRA та PEFT	Випадковий ліс	Трансформер для комп'ютерного зору
Галузь	Глибоке навчання	Глибоке навчання	Машинне навчання	Глибоке навчання
Родина	Machine learning	Machine learning	Machine learning	Machine learning
Рік появи	2019	2022	2001	2021
Автор методу	Devlin, J. et al.	Hu, E. J. et al.; Lester, B. et al.	Breiman, L.	Dosovitskiy, A. et al.
Тип	Transfer learning (fine-tuning a pre-trained transformer)	Parameter-efficient fine-tuning of large pretrained models	Ensemble (bagging of decision trees)	Transformer architecture for images (self-attention over patches)
Основоположне джерело	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL.	Hu, E. J. et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models. ICLR.	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.
Інші назви	BERT İnce Ayar (Fine-Tuning), BERT ince ayar, fine-tuning BERT, transfer learning with BERT	LoRA ve PEFT — Parametre Verimli İnce Ayar, Low-Rank Adaptation, parameter-efficient fine-tuning, prefix tuning	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Пов'язані	5	5	4	5
Підсумок	BERT fine-tuning, building on the BERT model introduced by Devlin and colleagues in 2019, re-trains a pre-trained BERT model on a small labelled dataset for a target task such as classification, named-entity recognition, or question answering. Through transfer learning it reaches high performance even with relatively little task-specific data.	LoRA (Low-Rank Adaptation), introduced by Hu et al. in 2022, and the broader family of parameter-efficient fine-tuning (PEFT) methods adapt large pretrained language models to new tasks by training only a small number of extra parameters instead of every weight in the model. This makes fine-tuning possible with far less GPU memory and compute while leaving the original model largely untouched.	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).