Pixel Deconvolutional Networks

Understand

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.