Mind the pad – CNNS can develop blind spots

May 4, 2021


We show how feature maps in convolutional networks are susceptible to spatial bias. Due to a combination of architectural choices, the activation at certain loca- tions is systematically elevated or weakened. The major source of this bias is the padding mechanism. Depending on several aspects of convolution arithmetic, this mechanism can apply the padding unevenly, leading to asymmetries in the learned weights. We demonstrate how such bias can be detrimental to certain tasks such as small object detection: the activation is suppressed if the stimulus lies in the impacted area, leading to blind spots and misdetection. We propose solutions to mitigate spatial bias and demonstrate how they can improve model accuracy.

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Written by

Bilal Alsallakh

Narine Kokhlikyan

Vivek Miglani

Jun Yuan

Orion Reblitz-Richardson


ICLR 2021

Research Topics

Core Machine Learning

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