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Presenter: Pouya Shiri
Supervisor:

Date: Wed, June 15, 2022
Time: 11:00:00 - 00:00:00
Place: ZOOM - Please see below.

ABSTRACT

Zoom link:  https://uvic.zoom.us/j/85792069315

Meeting ID: 857 9206 9315

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. 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.