On Translation Invariance in CNNs: Convolutional Layers Can Exploit Absolute Spatial Location


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.

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (CVPR 2020)
Osman Semih Kayhan
Osman Semih Kayhan
PhD Candidate

Interests include equivariant CNNs and context in object detection.

Jan van Gemert
Jan van Gemert
Associate Professor

Head of the CV Lab.