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| Wizualne uczenie kontrastowe× | Longformer / BigBird× | |
|---|---|---|
| Dziedzina | Uczenie głębokie | Uczenie głębokie |
| Rodzina | Machine learning | Machine learning |
| Rok powstania | 2020 | 2020 |
| Twórca≠ | Chen, T. et al. (SimCLR); He, K. et al. (MoCo) | Beltagy, Peters & Cohan (Longformer); Zaheer et al. (BigBird) |
| Typ≠ | Self-supervised deep representation learning | Sparse-attention Transformer for long sequences |
| Źródło pierwotne≠ | Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. ICML. link ↗ | Beltagy, I., Peters, M. E. & Cohan, A. (2020). Longformer: The Long-Document Transformer. arXiv. link ↗ |
| Inne nazwy≠ | Karşıtlık Öğrenmesi — Görsel (SimCLR / MoCo / BYOL), contrastive learning, self-supervised visual representation learning, SimCLR | Uzun Dizi Transformer (Longformer / BigBird), uzun dizi transformer, long-document transformer, sparse-attention transformer |
| Pokrewne≠ | 5 | 4 |
| Podsumowanie≠ | Visual contrastive learning is a self-supervised deep-learning approach — popularised by frameworks such as SimCLR (Chen et al., 2020) and MoCo (He et al., 2020) — that learns rich image representations without labels by pulling different augmentations of the same image together and pushing different images apart. It turns a large pool of unlabelled images into a useful feature extractor. | Long-sequence Transformers such as Longformer (Beltagy, Peters & Cohan, 2020) and BigBird (Zaheer et al., 2020) replace the standard Transformer's O(n²) attention with sparse attention patterns that scale linearly, O(n), with sequence length. This lets a single model attend over thousands of tokens — full documents, legal texts, or genomic sequences — that would not fit a conventional Transformer. |
| ScholarGateZbiór danych ↗ |
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