方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 胶囊网络× | 神经架构搜索× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份 | 2017 | 2017 |
| 提出者≠ | Sabour, S., Frosst, N. & Hinton, G. E. | Zoph, B. & Le, Q.V. |
| 类型≠ | Deep learning architecture (vector capsules with dynamic routing) | Automated architecture optimization (deep learning) |
| 开创性文献≠ | Sabour, S., Frosst, N. & Hinton, G. E. (2017). Dynamic Routing Between Capsules. Advances in Neural Information Processing Systems (NeurIPS). link ↗ | Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR. link ↗ |
| 别名 | Kapsül Ağı (CapsNet), CapsNet, capsule net, dynamic routing network | Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search |
| 相关≠ | 4 | 5 |
| 摘要≠ | A Capsule Network (CapsNet) is a deep learning architecture introduced by Sara Sabour, Nicholas Frosst and Geoffrey Hinton in 2017 that organises neurons as vectors (capsules) rather than scalar activations, so that spatial hierarchy and pose (orientation) information are encoded directly. It was proposed to overcome the fragility of convolutional networks to changes in viewpoint. | Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All. |
| ScholarGate数据集 ↗ |
|
|