Dilated convolutions, also known as atrous convolutions, have been widely
explored in deep convolutional neural networks (DCNNs) for various dense
prediction tasks. However, dilated convolutions suffer from the gridding
artifacts, which hampers the performance. In this work, we propose two simple
yet effective degridding methods by studying a decomposition of dilated
convolutions. Unlike existing models, which explore solutions by focusing on a
block of cascaded dilated convolutional layers, our methods address the
gridding artifacts by smoothing the dilated convolution itself. In addition, we
point out that the two degridding approaches are intrinsically related and
define separable and shared (SS) operations, which generalize the proposed
methods. We further explore SS operations in view of operations on graphs and
propose the SS output layer, which is able to smooth the entire DCNNs by only
replacing the output layer. We evaluate our degridding methods and the SS
output layer thoroughly, and visualize the smoothing effect through effective
receptive field analysis. Results show that our methods degridding yield
consistent improvements on the performance of dense prediction tasks, while
adding negligible amounts of extra training parameters. And the SS output layer
improves the performance significantly and is very efficient in terms of number
of training parameters.

本篇研究针对膨胀卷积存在的网络粗颗粒化问题，提出了两种去网格化方法和 shared and separable operations 和 SS output layer 的概念，并且经过有效接受域分析和实验验证，发现这些方法和概念能够有效地提高膨胀卷积神经网络的性能。

平滑膨胀卷积以提高密集预测性能

Smoothed Dilated Convolutions for Improved Dense Prediction

Convolutional networks for image classification progressively reduce
resolution until the image is represented by tiny feature maps in which the
spatial structure of the scene is no longer discernible. Such loss of spatial
acuity can limit image classification accuracy and complicate the transfer of
the model to downstream applications that require detailed scene understanding.
These problems can be alleviated by dilation, which increases the resolution of
output feature maps without reducing the receptive field of individual neurons.
We show that dilated residual networks (DRNs) outperform their non-dilated
counterparts in image classification without increasing the model's depth or
complexity. We then study gridding artifacts introduced by dilation, develop an
approach to removing these artifacts (`degridding'), and show that this further
increases the performance of DRNs. In addition, we show that the accuracy
advantage of DRNs is further magnified in downstream applications such as
object localization and semantic segmentation.

本文介绍了一种通过采用 dilated residual networks 提高图像分类准确性及在物体定位和语义分割等下游应用中的效果，同时探究了这种方法引入的网格伪影问题并提出了解决方法。