Módszerek összehasonlítása
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| Vision Transformer× | Variációs Autoencoder× | |
|---|---|---|
| Tudományterület | Mélytanulás | Mélytanulás |
| Módszercsalád | Machine learning | Machine learning |
| Keletkezés éve≠ | 2021 | 2014 |
| Megalkotó≠ | Dosovitskiy, A. et al. | Kingma, D. P. & Welling, M. |
| Típus≠ | Transformer architecture for images (self-attention over patches) | Deep generative latent-variable model (encoder–decoder) |
| Alapmű≠ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ |
| Alternatív nevek | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model |
| Kapcsolódó | 5 | 5 |
| Összefoglaló≠ | 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. |
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