Сравнение на методи

	LoRA и PEFT	Генеративна състезателна мрежа	Случайна гора	Вариационен автоенкодер
Област	Дълбоко обучение	Дълбоко обучение	Машинно обучение	Дълбоко обучение
Семейство	Machine learning	Machine learning	Machine learning	Machine learning
Година на възникване	2022	2014	2001	2014
Създател	Hu, E. J. et al.; Lester, B. et al.	Goodfellow, I. et al.	Breiman, L.	Kingma, D. P. & Welling, M.
Тип	Parameter-efficient fine-tuning of large pretrained models	Generative deep learning (adversarial two-network game)	Ensemble (bagging of decision trees)	Deep generative latent-variable model (encoder–decoder)
Основополагащ източник	Hu, E. J. et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models. ICLR.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Други названия	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	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Свързани	5	4	4	5
Резюме	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.	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 Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning.