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New Algorithm for Feature Learning on Encrypted Images

By Agbaeze Henry

Summary

Abstract— In this paper, I present a new learning algorithm
for extracting translation invariant features for image classification.
Using the golden ratio and a new filtering method that
collapses the pixel output from the convolution layer into a
learned feature representation, keeping its statistical properties
intact In this paper, I present a new learning algorithm for
extracting translation invariant features for image classification.
Using the golden ratio and a new filtering method that collapses
the pixel output from the convolution layer into a learned
feature representation, keeping its statistical properties intact.
New Algorithm for Feature Learning on Encrypted Images
 
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Published: June 28, 2018

Uploaded by: Agbaeze Henry

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Abstract

Abstract—In this paper, I present a new learning algorithm
for extracting translation invariant features for image classification.
Using the golden ratio and a new filtering method that
collapses the pixel output from the convolution layer into a
learned feature representation, keeping its statistical properties
intact In this paper, I present a new learning algorithm for
extracting translation invariant features for image classification.
Using the golden ratio and a new filtering method that collapses
the pixel output from the convolution layer into a learned
feature representation, keeping its statistical properties intact

About the Author

Agbaeze Henry

Agbaeze Henry

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