… tries to model part-whole relationships directly and figures out which part belongs to which
whole object in the place of pooling operation with dynamic routing so that the instantiate …

… task (such as object classification). The training algorithm of CapsNets involves a routing
mechanism between capsules in successive layers of the network that imitates hierarchical …

… implement capsule network to alleviate the aforementioned problems, ie, 1D-convolution
capsule network … , which can encode instantiation parameters of entity (an object or object part). …

… capsule networks is analyzed and summarized. Firstly, we summarize the shortcomings of
convolutional neural networks and introduce the basic concept of capsule network… the objects …

… objects. Even on the slightly more complex data set CIFAR10, the result of the capsule network
is only … Consequently, directly applying existing capsule networks to the gastrointestinal …

… -D convolutional neural networks (CNNs) … capsules in the lower layers to the most-related
capsule in the higher layer, dynamic routing (DR) is used to identify several overlapped objects …

… for six text classification benchmarks. More importantly, we show that capsule networks
achieves … transferring singlelabel to multi-label text classifications over strong baseline methods. …

… for scene classification. Capsule network [42] is usually composed of capsules where
each … multilevel feature representations for subsequent object classification. Consequently, …

… To reproduce the results of the study the capsule network layers (utilizing basic Hinton’s
dynamic routing mechanism, [11]) can be based on any open-sourced implementation like the …

… Indeed, a capsule (object) would only need to have connections to a reduced number of
lower capsules (parts of the object), and different objects might be formed by completely …