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| Mamba (model przestrzeni stanów)× | Swin Transformer× | Vision Transformer× | |
|---|---|---|---|
| Dziedzina | Uczenie głębokie | Uczenie głębokie | Uczenie głębokie |
| Rodzina | Machine learning | Machine learning | Machine learning |
| Rok powstania≠ | 2023 | 2021 | 2021 |
| Twórca≠ | Albert Gu | Ze Liu | Dosovitskiy, A. et al. |
| Typ≠ | Neural network architecture | Neural network architecture | Transformer architecture for images (self-attention over patches) |
| Źródło pierwotne≠ | Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956. link ↗ | Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin Transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 10012-10022). DOI ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Inne nazwy≠ | Mamba, State space models, Selective state space | Swin, Hierarchical Vision Transformer | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Pokrewne≠ | 4 | 4 | 5 |
| Podsumowanie≠ | Mamba is a sequence model architecture introduced by Gu and Dao in 2023 that achieves linear-time complexity while maintaining strong performance on language modeling tasks. By combining state space models with input-dependent selectivity, Mamba addresses the quadratic complexity of transformers while preserving modeling power. | The Swin Transformer is a hierarchical vision transformer introduced by Liu et al. in 2021 that uses shifted window attention to achieve computational efficiency while maintaining strong performance on computer vision tasks. Unlike the original Vision Transformer which applies global self-attention, Swin uses local window-based attention with periodic shifting to balance expressiveness and efficiency. | 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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