Improving Stability during Upsampling -- on the Importance of Spatial
  Context
Shashank Agnihotri Julia Grabinski Margret Keuper
State-of-the-art models for pixel-wise prediction tasks such as image
restoration, image segmentation, or disparity estimation, involve several
stages of data resampling, in which the resolution of feature maps is first
reduced to aggregate information and then sequentially increased to generate a
high-resolution output. Several previous works have investigated the effect of
artifacts that are invoked during downsampling and diverse cures have been
proposed that facilitate to improve prediction stability and even robustness
for image classification. However, equally relevant, artifacts that arise
during upsampling have been less discussed. This is significantly relevant as
upsampling and downsampling approaches face fundamentally different challenges.
While during downsampling, aliases and artifacts can be reduced by blurring
feature maps, the emergence of fine details is crucial during upsampling.
Blurring is therefore not an option and dedicated operations need to be
considered. In this work, we are the first to explore the relevance of context
during upsampling by employing convolutional upsampling operations with
increasing kernel size while keeping the encoder unchanged. We find that
increased kernel sizes can in general improve the prediction stability in tasks
such as image restoration or image segmentation, while a block that allows for
a combination of small-size kernels for fine details and large-size kernels for
artifact removal and increased context yields the best results.