Rapid Exact Signal Scanning with Deep Convolutional Neural Networks
Abstract
A rigorous formulation of the dynamics of a signal processing scheme aimed at dense signal scanning without any loss in accuracy is introduced and analyzed. Related methods proposed in the recent past lack a satisfactory analysis of whether they actually fulfill any exactness constraints. This is improved through an exact characterization of the requirements for a sound sliding window approach. The tools developed in this paper are especially beneficial if Convolutional Neural Networks are employed, but can also be used as a more general framework to validate related approaches to signal scanning. The proposed theory helps to eliminate redundant computations and renders special case treatment unnecessary, resulting in a dramatic boost in efficiency particularly on massively parallel processors. This is demonstrated both theoretically in a computational complexity analysis and empirically on modern parallel processors.
Cite
@article{arxiv.1508.06904,
title = {Rapid Exact Signal Scanning with Deep Convolutional Neural Networks},
author = {Markus Thom and Franz Gritschneder},
journal= {arXiv preprint arXiv:1508.06904},
year = {2017}
}
Comments
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