Deconvolutional layers have been widely used in a variety of deep models for
up-sampling, including encoder-decoder networks for semantic segmentation and
deep generative models for unsupervised learning. One of the key limitations of
deconvolutional operations is that they result in the so-called checkerboard
problem. This is caused by the fact that no direct relationship exists among
adjacent pixels on the output feature map. To address this problem, we propose
the pixel deconvolutional layer (PixelDCL) to establish direct relationships
among adjacent pixels on the up-sampled feature map. Our method is based on a
fresh interpretation of the regular deconvolution operation. The resulting
PixelDCL can be used to replace any deconvolutional layer in a plug-and-play
manner without compromising the fully trainable capabilities of original
models. The proposed PixelDCL may result in slight decrease in efficiency, but
this can be overcome by an implementation trick. Experimental results on
semantic segmentation demonstrate that PixelDCL can consider spatial features
such as edges and shapes and yields more accurate segmentation outputs than
deconvolutional layers. When used in image generation tasks, our PixelDCL can
largely overcome the checkerboard problem suffered by regular deconvolution
operations.

该研究提出了基于重构思想的像素反卷积层（PixelDCL），用于建立上采样后特征映射中相邻像素之间的直接关系，通过在语义分割和图像生成任务中的应用，可以有效地解决反卷积层存在的棋盘格问题，并提高图像分割的准确性。

像素反卷积网络

Pixel Deconvolutional Networks

Image restoration, including image denoising, super resolution, inpainting,
and so on, is a well-studied problem in computer vision and image processing,
as well as a test bed for low-level image modeling algorithms. In this work, we
propose a very deep fully convolutional auto-encoder network for image
restoration, which is a encoding-decoding framework with symmetric
convolutional-deconvolutional layers. In other words, the network is composed
of multiple layers of convolution and de-convolution operators, learning
end-to-end mappings from corrupted images to the original ones. The
convolutional layers capture the abstraction of image contents while
eliminating corruptions. Deconvolutional layers have the capability to upsample
the feature maps and recover the image details. To deal with the problem that
deeper networks tend to be more difficult to train, we propose to symmetrically
link convolutional and deconvolutional layers with skip-layer connections, with
which the training converges much faster and attains better results.

本文提出一种使用对称卷积 - 反卷积层的深度全卷积自编码器网络用于图像修复，通过跳级连接缓解深度网络训练难的问题，达到了更快收敛和更好的修复效果。