Comparer des méthodes

	LoRA et PEFT	Réseau antagoniste génératif
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2022	2014
Auteur d'origine	Hu, E. J. et al.; Lester, B. et al.	Goodfellow, I. et al.
Type	Parameter-efficient fine-tuning of large pretrained models	Generative deep learning (adversarial two-network game)
Source fondatrice	Hu, E. J. et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models. ICLR.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
Alias	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
Apparentées	5	4
Résumé	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.