Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| BERT-finjustering× | Random Forest× | Vision Transformer× | |
|---|---|---|---|
| Ämnesområde≠ | Djupinlärning | Maskininlärning | Djupinlärning |
| Familj | Machine learning | Machine learning | Machine learning |
| Ursprungsår≠ | 2019 | 2001 | 2021 |
| Upphovsperson≠ | Devlin, J. et al. | Breiman, L. | Dosovitskiy, A. et al. |
| Typ≠ | Transfer learning (fine-tuning a pre-trained transformer) | Ensemble (bagging of decision trees) | Transformer architecture for images (self-attention over patches) |
| Ursprungskälla≠ | Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL. DOI ↗ | 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 ↗ |
| Alias | BERT İnce Ayar (Fine-Tuning), BERT ince ayar, fine-tuning BERT, transfer learning with BERT | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Närliggande≠ | 5 | 4 | 5 |
| Sammanfattning≠ | BERT fine-tuning, building on the BERT model introduced by Devlin and colleagues in 2019, re-trains a pre-trained BERT model on a small labelled dataset for a target task such as classification, named-entity recognition, or question answering. Through transfer learning it reaches high performance even with relatively little task-specific data. | 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). |
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