Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Mamba (модел с отворено състояние)× | Vision Mamba× | |
|---|---|---|
| Област | Дълбоко обучение | Дълбоко обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2023 | 2024 |
| Създател≠ | Albert Gu | Li Zhu |
| Тип | Neural network architecture | Neural network architecture |
| Основополагащ източник≠ | 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 ↗ |
| Други названия≠ | Mamba, State space models, Selective state space | ViM, Mamba for Vision |
| Свързани | 4 | 4 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
|
|