Improving Translation Invariance in Convolutional Neural Networks with
  Peripheral Prediction Padding
Kensuke Mukai Takao Yamanaka
Zero padding is often used in convolutional neural networks to prevent the
feature map size from decreasing with each layer. However, recent studies have
shown that zero padding promotes encoding of absolute positional information,
which may adversely affect the performance of some tasks. In this work, a novel
padding method called Peripheral Prediction Padding (PP-Pad) method is
proposed, which enables end-to-end training of padding values suitable for each
task instead of zero padding. Moreover, novel metrics to quantitatively
evaluate the translation invariance of the model are presented. By evaluating
with these metrics, it was confirmed that the proposed method achieved higher
accuracy and translation invariance than the previous methods in a semantic
segmentation task.