# Improving Translation Invariance in Convolutional Neural Networks with Peripheral Prediction Padding Source: [Improving Translation Invariance in Convolutional Neural Networks with Peripheral Prediction Padding]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:32:16.690Z Authors: Kensuke Mukai, Takao Yamanaka Published: 2023-07-15T06:44:34.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()