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| Random Forest× | Vision Transformer× | |
|---|---|---|
| Dziedzina≠ | Uczenie maszynowe | Uczenie głębokie |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2001 | 2021 |
| Twórca≠ | Breiman, L. | Dosovitskiy, A. et al. |
| Typ≠ | Ensemble (bagging of decision trees) | Transformer architecture for images (self-attention over patches) |
| Źródło pierwotne≠ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Inne nazwy | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Pokrewne≠ | 4 | 5 |
| Podsumowanie≠ | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. | 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). |
| ScholarGateZbiór danych ↗ |
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