# Efficient Smoothing of Dilated Convolutions for Image Segmentation

arXiv: https://arxiv.org/abs/1903.07992 · HTML: https://mlsanity.com/paper/1903.07992/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [Towards Diverse Binary Segmentation via A Simple yet General Gated Network](https://mlsanity.com/paper/2303.10396/) (2023)
- [FDNet: Focal Decomposed Network for Efficient, Robust and Practical Time Series Forecasting](https://mlsanity.com/paper/2306.10703/) (2023)
- [Empirical Study of Multi-Task Hourglass Model for Semantic Segmentation Task](https://mlsanity.com/paper/2105.13531/) (2021)

## Abstract (arXiv)

Dilated Convolutions have been shown to be highly useful for the task of image segmentation. By introducing gaps into convolutional filters, they enable the use of larger receptive fields without increasing the original kernel size. Even though this allows for the inexpensive capturing of features at different scales, the structure of the dilated convolutional filter leads to a loss of information. We hypothesise that inexpensive modifications to Dilated Convolutional Neural Networks, such as additional averaging layers, could overcome this limitation. In this project we test this hypothesis by evaluating the effect of these modifications for a state-of-the art image segmentation system and compare them to existing approaches with the same objective. Our experiments show that our proposed methods improve the performance of dilated convolutions for image segmentation. Crucially, our modifications achieve these results at a much lower computational cost than previous smoothing approaches.