Mind the Pad -- CNNs can Develop Blind Spots

We show how feature maps in convolutional networks are susceptible to spatial bias. Due to a combination of architectural choices, the activation at certain locations 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. Convolutional neural networks (CNNs) have become state-of-the-art feature extractors for a wide variety of machine-learning tasks. A large body of work has focused on understanding the feature maps a CNN computes for an input. However, little attention has been paid to the spatial distribution of activation in the maps. Our interest in analyzing this distribution is triggered by mysterious failure cases of a traffic light detector: The detector is able to detect a small but visible traffic light with a high score in one frame of a road scene sequence. However, it fails completely in detecting the same traffic light in the next frame captured by the ego-vehicle.