Comparar métodos

	LoRA e PEFT	Autoencoder Variacional	Vision Transformer
Área	Aprendizado profundo	Aprendizado profundo	Aprendizado profundo
Família	Machine learning	Machine learning	Machine learning
Ano de origem	2022	2014	2021
Autor original	Hu, E. J. et al.; Lester, B. et al.	Kingma, D. P. & Welling, M.	Dosovitskiy, A. et al.
Tipo	Parameter-efficient fine-tuning of large pretrained models	Deep generative latent-variable model (encoder–decoder)	Transformer architecture for images (self-attention over patches)
Fonte seminal	Hu, E. J. et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models. ICLR.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Outros nomes	LoRA ve PEFT — Parametre Verimli İnce Ayar, Low-Rank Adaptation, parameter-efficient fine-tuning, prefix tuning	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Relacionados	5	5	5
Resumo	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.	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.	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).