Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Mamba (модел с отворено състояние)× | Невронни полета на излъчване (NeRF)× | |
|---|---|---|
| Област | Дълбоко обучение | Дълбоко обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2023 | 2020 |
| Създател≠ | Albert Gu | Ben Mildenhall |
| 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 ↗ | Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020). NeRF: Representing scenes as neural radiance fields for view synthesis. In Computer Vision-ECCV 2020: 16th European Conference (pp. 405-421). Springer International Publishing. DOI ↗ |
| Други названия≠ | Mamba, State space models, Selective state space | NeRF, Neural radiance field |
| 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. | Neural Radiance Fields (NeRF) is a method introduced by Mildenhall et al. in 2020 that represents a 3D scene as a continuous function parameterized by a neural network. Given multi-view images of a scene, NeRF learns to predict the color and density of light rays at any spatial location and viewing angle, enabling novel view synthesis with photorealistic quality. |
| ScholarGateНабор от данни ↗ |
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