… ] and lung tumor classification [18]. In this study, we propose a Capsule Network-based
framework, referred to as the COVID-CAPS, for COVID-19 identification using X-ray images. The …

… This research proposes a novel capsule network based model for VLR image recognition.
Hinton et al. [7] proposed learning “capsules”, which represent a vector of instantiation …

… Several studies have investigated the accuracy of convolutional network depth for general
image classification and obtained significant improvement by increasing network depth [10]. …

… Classification of hierarchical multi-label text is performed with a simple capsule network …
The capsules are associated with each digit in handwritten digit image classification task [10…

… This paper aims to detect the diseases in grape leaves using convolutional capsule networks.
The capsule network is a promising neural network in deep learning. This network uses a …

… excellent performance of capsule network in image classification, we design a capsule
network combined with our compositional weighted coding method for text classification. We also …

… hyperspectral image classification… classification task. To further explore the contribution of
spectral and spatial features, we propose a classification framework based on capsule network …

… of a large number of images, deep learning has been widely applied in the field of computer
vision, and there are many tasks in different directions, such as image classification [1]-[3], …

… What’s more, deep learning models are highly repurposable in nature: we can use image
classification or speech-to-text models trained on large-scale datasets and reuse them on a …

… a garbage image classification algorithm called … capsule module in the capsule network
structure to improve the accuracy and robustness of the model on the garbage classification task. …