[HTML][HTML] Capsule networks–a survey
Modern day computer vision tasks requires efficient solution to problems such as image
recognition, natural language processing, object detection, object segmentation and …
recognition, natural language processing, object detection, object segmentation and …
A novel Capsule Neural Network based model for drowsiness detection using electroencephalography signals
The early detection of drowsiness has become vital to ensure the correct and safe
development of several industries' tasks. Due to the transient mental state of a human …
development of several industries' tasks. Due to the transient mental state of a human …
Stacked capsule autoencoders
Abstract Objects are composed of a set of geometrically organized parts. We introduce an
unsupervised capsule autoencoder (SCAE), which explicitly uses geometric relationships …
unsupervised capsule autoencoder (SCAE), which explicitly uses geometric relationships …
Radiologist-level covid-19 detection using ct scans with detail-oriented capsule networks
Radiographic images offer an alternative method for the rapid screening and monitoring of
Coronavirus Disease 2019 (COVID-19) patients. This approach is limited by the shortage of …
Coronavirus Disease 2019 (COVID-19) patients. This approach is limited by the shortage of …
Capsule routing via variational bayes
Capsule networks are a recently proposed type of neural network shown to outperform
alternatives in challenging shape recognition tasks. In capsule networks, scalar neurons are …
alternatives in challenging shape recognition tasks. In capsule networks, scalar neurons are …
How can we be so dense? the benefits of using highly sparse representations
S Ahmad, L Scheinkman - arXiv preprint arXiv:1903.11257, 2019 - arxiv.org
Most artificial networks today rely on dense representations, whereas biological networks
rely on sparse representations. In this paper we show how sparse representations can be …
rely on sparse representations. In this paper we show how sparse representations can be …
Equivariant transformer networks
How can prior knowledge on the transformation invariances of a domain be incorporated
into the architecture of a neural network? We propose Equivariant Transformers (ETs), a …
into the architecture of a neural network? We propose Equivariant Transformers (ETs), a …
Unsupervised part representation by flow capsules
Capsule networks aim to parse images into a hierarchy of objects, parts and relations. While
promising, they remain limited by an inability to learn effective low level part descriptions. To …
promising, they remain limited by an inability to learn effective low level part descriptions. To …
Capsule networks–A survey
M Kwabena Patrick, A Felix Adekoya, A Abra Mighty… - 2022 - dl.acm.org
Modern day computer vision tasks requires efficient solution to problems such as image
recognition, natural language processing, object detection, object segmentation and …
recognition, natural language processing, object detection, object segmentation and …
Improving the robustness of capsule networks to image affine transformations
Convolutional neural networks (CNNs) achieve translational invariance by using pooling
operations. However, the operations do not preserve the spatial relationships in the learned …
operations. However, the operations do not preserve the spatial relationships in the learned …