Pouya Shiri
ABSTRACT
Capsule Networks (CapsNets) are the new generation of classifiers with several advantages over the previous ones. Such advantages include higher robustness to affine transformed datasets and detection of overlapping images. CapsNets, while obtaining state-of-the-art accuracy on the MNIST digit recognition dataset, fall behind Convolutional Neural Networks (CNNs) for other datasets. Moreover, CapsNets are slow compared to CNNs. In this work, we propose Convolutional Fully Connected (CFC) CapsNet as an alternative enhanced architecture to conventional CapsNet [8]. CFC-CapsNet is a more efficient network: training and testing are performed faster and a slightly higher accuracy is achieved compared to the conventional CapsNet. CFC-CapsNet includes fewer trainable weights (parameters) and therefore is more efficient in terms of memory usage. The code for CFC-CapsNet is available on Github 1.
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- Convolutional Fully-Connected Capsule Network (CFC-CapsNet)
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