On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations

On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations

Deep convolutional neural networks are generally regarded as robust function approximators. So far, this intuition is based on perturbations to external stimuli such as the images to be classified. Here we explore the robustness of convolutional neural networks to perturbations to the internal weigh...

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Bibliographic Details
Main Authors:	Cheney, Nicholas, Schrimpf, Martin, Kreiman, Gabriel
Format:	Technical Report
Language:	en_US
Published:	Center for Brains, Minds and Machines (CBMM), arXiv 2017
Online Access:	http://hdl.handle.net/1721.1/107935

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