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| Fine-Tuning BERT× | Transformer Visi× | |
|---|---|---|
| Bidang | Pembelajaran Mendalam | Pembelajaran Mendalam |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2019 | 2021 |
| Pengasas≠ | Devlin, J. et al. | Dosovitskiy, A. et al. |
| Jenis≠ | Transfer learning (fine-tuning a pre-trained transformer) | Transformer architecture for images (self-attention over patches) |
| Sumber perintis≠ | Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL. 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 | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Berkaitan | 5 | 5 |
| Ringkasan≠ | 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. | 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). |
| ScholarGateSet data ↗ |
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