Comparer des méthodes

	Capsule Network	Recherche d'architecture neuronale	Forêt Aléatoire
Domaine	Apprentissage profond	Apprentissage profond	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	2017	2017	2001
Auteur d'origine	Sabour, S., Frosst, N. & Hinton, G. E.	Zoph, B. & Le, Q.V.	Breiman, L.
Type	Deep learning architecture (vector capsules with dynamic routing)	Automated architecture optimization (deep learning)	Ensemble (bagging of decision trees)
Source fondatrice	Sabour, S., Frosst, N. & Hinton, G. E. (2017). Dynamic Routing Between Capsules. Advances in Neural Information Processing Systems (NeurIPS).	Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	Kapsül Ağı (CapsNet), CapsNet, capsule net, dynamic routing network	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	4	5	4
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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.