… spatial relationships in images. Furthermore, … capsule network when labelled samples are
limited. The Pavia University and Indian Pines images are used to evaluate the classification …

… image reconstruction and SegCaps [12] (a 14layer convolutional-deconvolutional capsule
network) outperforms U-Net in medical image … -10 dataset on image classification task, which …

… GCaps [18] is a group equivariant capsule network. Although the aforementioned CapsNets
… to simple image classification tasks. The inverted dot-product attention capsule network (…

… rics that are rarely used to evaluate capsule network models in practice. This paper, however,
focuses on the metrics that can be used to further enhance the reliability and trust of the …

… Image classification is a key step in several computer visual tasks. Capsule network, as an
… In this paper, a novel capsule network combined with the original capsule network and multi-…

… For many attention mechanisms applied to image classification tasks, the last activation
function mostly uses softmax or sigmoid, while the tanh function is used in experiments. The …

… Due to its outperformance and robustness, CNN has been widely used in diverse fields such
as object recognition, image classification, and speech recognition [7]. In the field of remote …

… in medical image classification is nothing new. Researchers around the world are utilizing
Convolutional Neural Networks to solve common problems in the field of image classification. …

… In this section, we propose DenseCaps, an image classification model based on the capsule
network and dense connections, utilizing multi-level feature-capsules to enrich and perfect …

… We aim to simply demonstrate that a properly working capsule network should achieve
higher results with a considerably lower number of parameters due to its intrinsic capability to …