7 citations · 13 across the 5 of their papers we have counts for
5 papers · 1 filter
Capsule Network Projectors are Equivariant and Invariant Learners
Miles Everett, Aiden Durrant, Mingjun Zhong +1
Learning invariant representations has been the long-standing approach to self-supervised learning. However, recently progress has been made in preserving equivariant properties in…
Masked Capsule Autoencoders
Miles Everett, Mingjun Zhong, Georgios Leontidis
We propose Masked Capsule Autoencoders (MCAE), the first Capsule Network that utilises pretraining in a modern self-supervised paradigm, specifically the masked image modelling fra…
ProtoCaps: A Fast and Non-Iterative Capsule Network Routing Method
Miles Everett, Mingjun Zhong, Georgios Leontidis
Capsule Networks have emerged as a powerful class of deep learning architectures, known for robust performance with relatively few parameters compared to Convolutional Neural Netwo…
Vanishing Activations: A Symptom of Deep Capsule Networks
Miles Everett, Mingjun Zhong, Georgios Leontidis
Capsule Networks, an extension to Neural Networks utilizing vector or matrix representations instead of scalars, were initially developed to create a dynamic parse tree where visua…
Learning with Capsules: A Survey
Fabio De Sousa Ribeiro, Kevin Duarte, Miles Everett +2
Capsule networks were proposed as an alternative approach to Convolutional Neural Networks (CNNs) for learning object-centric representations, which can be leveraged for improved g…