Σύγκριση μεθόδων

	Δίκτυο Κάψουλας	Αναζήτηση Νευρωνικής Αρχιτεκτονικής
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2017	2017
Δημιουργός	Sabour, S., Frosst, N. & Hinton, G. E.	Zoph, B. & Le, Q.V.
Τύπος	Deep learning architecture (vector capsules with dynamic routing)	Automated architecture optimization (deep learning)
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	Kapsül Ağı (CapsNet), CapsNet, capsule net, dynamic routing network	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search
Συναφείς	4	5
Σύνοψη	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.