Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Red Neuronal de Cápsulas× | Búsqueda de Arquitecturas Neuronales× | |
|---|---|---|
| Campo | Aprendizaje profundo | Aprendizaje profundo |
| Familia | Machine learning | Machine learning |
| Año de origen | 2017 | 2017 |
| Autor original≠ | Sabour, S., Frosst, N. & Hinton, G. E. | Zoph, B. & Le, Q.V. |
| Tipo≠ | Deep learning architecture (vector capsules with dynamic routing) | Automated architecture optimization (deep learning) |
| Fuente seminal≠ | 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 ↗ |
| Alias | Kapsül Ağı (CapsNet), CapsNet, capsule net, dynamic routing network | Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search |
| Relacionados≠ | 4 | 5 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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