title: Pixel Deconvolutional Networks
abstract: 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.
authors: Hongyang Gao Hao Yuan Zhengyang Wang Shuiwang Ji
hIndex: Shuiwang Ji 45