CapsNet Model

Capsule Networks (CapsNets) are a type of neural network designed to improve upon the limitations of convolutional neural networks (CNNs) by learning more robust, pose-aware, and interpretable object representations. Current research focuses on enhancing CapsNet efficiency through techniques like sparse attention routing, orthogonal weight matrices, and parallel dynamic routing, as well as exploring their application in diverse fields such as time series analysis, medical image segmentation, and financial forecasting. The improved robustness and interpretability of CapsNets offer significant advantages for applications requiring reliable performance in the presence of noise or variations in input data, while ongoing research addresses challenges related to scalability and computational cost.