Salīdzināt metodes

	LoRA un PEFT	Generatīvais Adversariālais Tīkls	Vision Transformer
Nozare	Dziļā mācīšanās	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning	Machine learning
Izcelsmes gads	2022	2014	2021
Autors	Hu, E. J. et al.; Lester, B. et al.	Goodfellow, I. et al.	Dosovitskiy, A. et al.
Tips	Parameter-efficient fine-tuning of large pretrained models	Generative deep learning (adversarial two-network game)	Transformer architecture for images (self-attention over patches)
Pirmavots	Hu, E. J. et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models. ICLR.	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.
Citi nosaukumi	LoRA ve PEFT — Parametre Verimli İnce Ayar, Low-Rank Adaptation, parameter-efficient fine-tuning, prefix tuning	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Saistītās	5	4	5
Kopsavilkums	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.	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).