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| Vision Transformer× | Diffusion Model× | Support Vector Machine (Klassifikation)× | |
|---|---|---|---|
| Fachgebiet≠ | Deep Learning | Deep Learning | Maschinelles Lernen |
| Familie | Machine learning | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2021 | 2020 | 1995 |
| Urheber≠ | Dosovitskiy, A. et al. | Ho, J., Jain, A. & Abbeel, P. | Cortes, C. & Vapnik, V. |
| Typ≠ | Transformer architecture for images (self-attention over patches) | Generative deep learning (denoising diffusion) | Maximum-margin classifier (kernel method) |
| Wegweisende Quelle≠ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ | Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS. link ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Aliasnamen≠ | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images | Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Verwandt≠ | 5 | 4 | 5 |
| Zusammenfassung≠ | 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). | A diffusion model is a generative deep-learning method, introduced by Ho, Jain and Abbeel in 2020 (DDPM), that learns to produce high-quality images, audio and molecular structures by reversing a step-by-step noising process. It has largely displaced GANs as the current state of the art in generative modelling. | The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data. |
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