We study a referential game (a type of signaling game) where two agents communicate with each other via a discrete bottleneck to achieve a common goal.
Capsule networks (CapsNets) have recently shown promise to excel in most computer vision tasks, especially pertaining to scene understanding.
Privacy issues related to video camera feeds have led to a growing need for suitable alternatives that provide functionalities such as user authentication, activity classification and tracking in a noninvasive manner.
Capsule networks excel in understanding spatial relationships in 2D data for vision related tasks.
In addition to the uses by individual developers, SmartEmbed can also be applied to studies of smart contracts in a large scale.
Capsule Network is a promising concept in deep learning, yet its true potential is not fully realized thus far, providing sub-par performance on several key benchmark datasets with complex data.
Our system is useful in character recognition for localized languages that lack much labeled training data and even in other related more general contexts such as object recognition.
Ranked #3 on Image Classification on EMNIST-Letters
We propose three schemas for combining static and motion components: based on a variance ratio, principal components, and Cholesky decomposition.