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US Patent 10185891 Systems and methods for compact convolutional neural networks

Patent 10185891 was granted and assigned to GoPro on January, 2019 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
GoPro
GoPro
Current Assignee
GoPro
GoPro
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10185891
Date of Patent
January 22, 2019
Patent Application Number
15206150
Date Filed
July 8, 2016
Patent Citations Received
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US Patent 11928860 On the fly adaptive convolutional neural network for variable computational budget
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US Patent 11380081 Image processing apparatus and operating method of the same
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US Patent 11392822 Image processing method, image processing apparatus, and computer-readable storage medium
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US Patent 11875260 Reducing architectural complexity of convolutional neural networks via channel pruning
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US Patent 10339445 Implementation of ResNet in a CNN based digital integrated circuit
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US Patent 10360470 Implementation of MobileNet in a CNN based digital integrated circuit
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US Patent 10360494 Convolutional neural network (CNN) system based on resolution-limited small-scale CNN modules
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US Patent 10366328 Approximating fully-connected layers with multiple arrays of 3x3 convolutional filter kernels in a CNN based integrated circuit
...
Patent Primary Examiner
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Aaron W Carter
Patent abstract

A compact convolutional neural network may include a preliminary layer group, one or more intermediate layer groups, a final layer group, and/or other layers/layer groups. The preliminary layer group may include an input layer, a first preliminary normalization layer, a preliminary padding layer, a preliminary convolution layer, a preliminary activation layer, a second preliminary normalization layer, and a preliminary downsampling layer. One or more intermediate layer groups may include an intermediate squeeze layer, a first intermediate normalization layer, an intermediate padding layer, a first intermediate expand layer, a second intermediate expand layer, an intermediate concatenation layer, a second intermediate normalization layer, an intermediate activation layer, and an intermediate combination layer. The final layer group may include a final dropout layer, a final convolution layer, a final activation layer, a first final normalization layer, a final downsampling layer, a final flatten layer, and a second final normalization layer.

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