So sánh phương pháp

	Transformer Thị giác	Bộ tự mã hóa biến phân
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2021	2014
Người khởi xướng	Dosovitskiy, A. et al.	Kingma, D. P. & Welling, M.
Loại	Transformer architecture for images (self-attention over patches)	Deep generative latent-variable model (encoder–decoder)
Công trình gốc	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Tên gọi khác	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Liên quan	5	5
Tóm tắt	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).	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.