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http://dx.doi.org/10.21289/KSIC.2021.24.1.1

Feature Visualization and Error Rate Using Feature Map by Convolutional Neural Networks  

Jin, Taeseok (Dept. of Mechatronics Ph.D Dongseo University)
Publication Information
Journal of the Korean Society of Industry Convergence / v.24, no.1, 2021 , pp. 1-7 More about this Journal
Abstract
In this paper, we presented the experimental basis for the theoretical background and robustness of the Convolutional Neural Network for object recognition based on artificial intelligence. An experimental result was performed to visualize the weighting filters and feature maps for each layer to determine what characteristics CNN is automatically generating. experimental results were presented on the trend of learning error and identification error rate by checking the relevance of the weight filter and feature map for learning error and identification error. The weighting filter and characteristic map are presented as experimental results. The automatically generated characteristic quantities presented the results of error rates for moving and rotating robustness to geometric changes.
Keywords
CNN; Robustness; Deep learning; Image processing; Sparse Connection;
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