# Partial Convolution based Padding

Source: [Partial Convolution based Padding](<https://www.sota2.com/research/paper/68fa651e1e209131cea0881f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:19:52.933Z

Authors: Guilin Liu, Kevin J. Shih, Ting-Chun Wang, Fitsum A. Reda, Karan Sapra, Zhiding Yu, Andrew Tao, Bryan Catanzaro

Published: 2018-11-28T18:13:02.000Z

## Abstract

In this paper, we present a simple yet effective padding scheme that can be used as a drop-in module for existing convolutional neural networks. We call it partial convolution based padding, with the intuition that the padded region can be treated as holes and the original input as non-holes. Specifically, during the convolution operation, the convolution results are re-weighted near image borders based on the ratios between the padded area and the convolution sliding window area. Extensive experiments with various deep network models on ImageNet classification and semantic segmentation demonstrate that the proposed padding scheme consistently outperforms standard zero padding with better accuracy.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1811.11718>)

[PDF](<https://arxiv.org/pdf/1811.11718.pdf>)