Methoden vergelijken

	Transformator voor Visuele Waarneming	Variational Autoencoder
Vakgebied	Deep learning	Deep learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	2021	2014
Grondlegger	Dosovitskiy, A. et al.	Kingma, D. P. & Welling, M.
Type	Transformer architecture for images (self-attention over patches)	Deep generative latent-variable model (encoder–decoder)
Oorspronkelijke bron	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).
Aliassen	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Verwant	5	5
Samenvatting	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.