Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Mamba (Modelo de Espaço de Estados)× | Vision Mamba× | Vision Transformer× | |
|---|---|---|---|
| Área | Aprendizado profundo | Aprendizado profundo | Aprendizado profundo |
| Família | Machine learning | Machine learning | Machine learning |
| Ano de origem≠ | 2023 | 2024 | 2021 |
| Autor original≠ | Albert Gu | Li Zhu | Dosovitskiy, A. et al. |
| Tipo≠ | Neural network architecture | Neural network architecture | Transformer architecture for images (self-attention over patches) |
| Fonte seminal≠ | Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956. link ↗ | Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., & Wang, X. (2024). Vision Mamba: Efficient state space models for image understanding. In International Conference on Machine Learning. link ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Outros nomes≠ | Mamba, State space models, Selective state space | ViM, Mamba for Vision | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Relacionados≠ | 4 | 4 | 5 |
| Resumo≠ | 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. | Vision Mamba is an efficient state space model approach for image understanding introduced in 2024 that adapts Mamba, a linear-complexity sequence model, to computer vision. By reformulating image tokens as sequences and using state space models, Vision Mamba achieves competitive accuracy with transformers while maintaining linear computational complexity. | 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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