  Pixel-wise predictions are required in a wide variety of tasks such as image
restoration, image segmentation, or disparity estimation. Common models involve
several stages of data resampling, in which the resolution of feature maps is
first reduced to aggregate information and then increased to generate a
high-resolution output. Previous works have shown that resampling operations
are subject to artifacts such as aliasing. During downsampling, aliases have
been shown to compromise the prediction stability of image classifiers. During
upsampling, they have been leveraged to detect generated content. Yet, the
effect of aliases during upsampling has not yet been discussed w.r.t. the
stability and robustness of pixel-wise predictions. While falling under the
same term (aliasing), the challenges for correct upsampling in neural networks
differ significantly from those during downsampling: when downsampling, some
high frequencies can not be correctly represented and have to be removed to
avoid aliases. However, when upsampling for pixel-wise predictions, we actually
require the model to restore such high frequencies that can not be encoded in
lower resolutions. The application of findings from signal processing is
therefore a necessary but not a sufficient condition to achieve the desirable
output. In contrast, we find that the availability of large spatial context
during upsampling allows to provide stable, high-quality pixel-wise
predictions, even when fully learning all filter weights.
