Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Capsule Network× | Recherche d'architecture neuronale× | |
|---|---|---|
| Domaine | Apprentissage profond | Apprentissage profond |
| Famille | Machine learning | Machine learning |
| Année d'origine | 2017 | 2017 |
| Auteur d'origine≠ | Sabour, S., Frosst, N. & Hinton, G. E. | Zoph, B. & Le, Q.V. |
| Type≠ | Deep learning architecture (vector capsules with dynamic routing) | Automated architecture optimization (deep learning) |
| Source fondatrice≠ | 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 |
| Apparentées≠ | 4 | 5 |
| Résumé≠ | 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. |
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