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Deep Quaternion Networks
The field of deep learning has seen significant advancement in recent years.
However, much of the existing work has been focused on real-valued numbers.
Recent work has shown that a deep learning system using the complex numbers can
be deeper for a fixed parameter budget compared to its real-valued counterpart.
In this work, we explore the benefits of generalizing one step further into the
hyper-complex numbers, quaternions specifically, and provide the architecture
components needed to build deep quaternion networks. We develop the theoretical
basis by reviewing quaternion convolutions, developing a novel quaternion
weight initialization scheme, and developing novel algorithms for quaternion
batch-normalization. These pieces are tested in a classification model by
end-to-end training on the CIFAR-10 and CIFAR-100 data sets and a segmentation
model by end-to-end training on the KITTI Road Segmentation data set. These
quaternion networks show improved convergence compared to real-valued and
complex-valued networks, especially on the segmentation task, while having
fewer parametersComment: IJCNN 2018, 8 pages, 1 figur
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