2022
DOI: 10.48550/arxiv.2201.02711
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Block Walsh-Hadamard Transform Based Binary Layers in Deep Neural Networks

Abstract: Convolution has been the core operation of modern deep neural networks. It is well-known that convolutions can be implemented in the Fourier Transform domain. In this paper, we propose to use binary block Walsh-Hadamard transform (WHT) instead of the Fourier transform. We use WHT-based binary layers to replace some of the regular convolution layers in deep neural networks. We utilize both one-dimensional (1-D) and two-dimensional (2-D) binary WHTs in this paper. In both 1-D and 2-D layers, we compute the binar… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 26 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?