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Density of Top-Layer Codes in Deep Convolutional Neural Networks Trained for Face Identification

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Acknowledgements This research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA R&D Contract No. 2014-14071600012.… Click to show full abstract

Acknowledgements This research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA R&D Contract No. 2014-14071600012. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Deep Convolutional Neural Networks (DCNNs) •Robust across image conditions (view, illumination, etc.) •Modeled after primate ventral visual stream [1,2]

Keywords: top layer; neural networks; deep convolutional; convolutional neural; layer codes; density top

Journal Title: Journal of Vision
Year Published: 2019

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