| Summary: | Capsule Network (CapsNet) is a methodology with good prospects in visual tasks, since it can keep a stronger relationship of spatial information than Convolutional Neural Networks (CNNs). However, the current Capsule Network do not provide performance as expected on several benchmark data sets with complex data and backgrounds. Inspired by the multiple capsules of Diverse Capsule Network (DCNet++) and the Spatial Group-wise Enhance (SGE) mechanism, we propose the Diverse Enhanced Capsule Network (DE-CapsNet), a hierarchical architecture which uses residual convolutional layers and the position-wise dot product to build diverse enhanced primary capsules with various scales of images for complex data. The architecture adopts the Sigmoid function in a dynamic routing algorithm to get a more uniform distribution of routing coefficients which obviously distinguishes the assignment probabilities between capsules. DE-CapsNet achieved state-of-the-art accuracy on Canadian Institute For Advanced Research (CIFAR-10) in the Capsule Network field and provided better performance than the ensemble of seven CapsNets on Fashion-Modified National Institue of Standards and Technology database (F-MNIST) while achieving a 50.3% reduction in the number of parameters.
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