In this paper we challenge the common assumption that convolutional layers in
modern CNNs are translation invariant. We show that CNNs can and will exploit
the absolute spatial location by learning filters that respond exclusively to
particular absolute locations by exploiting image boundary effects. Because
modern CNNs filters have a huge receptive field, these boundary effects operate
even far from the image boundary, allowing the network to exploit absolute
spatial location all over the image. We give a simple solution to remove
spatial location encoding which improves translation invariance and thus gives
a stronger visual inductive bias which particularly benefits small data sets.
We broadly demonstrate these benefits on several architectures and various
applications such as image classification, patch matching, and two video
classification datasets.

本文挑战了现代 CNN 中卷积层具有平移不变性的普遍假设，并展示了 CNN 可以和将利用绝对空间位置来学习仅响应于特定绝对位置的滤波器，进而利用图像边界效应来达到效果。我们提供了一个简单的解决方案来消除空间位置编码，从而提高平移不变性，因此在小数据集方面提供更强的视觉归纳偏差。我们在几种体系结构和各种应用程序（例如图像分类、补丁匹配和两个视频分类数据集）中广泛展示了这些好处。