Masked Discriminators for Content-Consistent Unpaired Image-to-Image
  Translation
Bonifaz Stuhr Jürgen Brauer Bernhard Schick Jordi Gonzàlez
A common goal of unpaired image-to-image translation is to preserve content
consistency between source images and translated images while mimicking the
style of the target domain. Due to biases between the datasets of both domains,
many methods suffer from inconsistencies caused by the translation process.
Most approaches introduced to mitigate these inconsistencies do not constrain
the discriminator, leading to an even more ill-posed training setup. Moreover,
none of these approaches is designed for larger crop sizes. In this work, we
show that masking the inputs of a global discriminator for both domains with a
content-based mask is sufficient to reduce content inconsistencies
significantly. However, this strategy leads to artifacts that can be traced
back to the masking process. To reduce these artifacts, we introduce a local
discriminator that operates on pairs of small crops selected with a similarity
sampling strategy. Furthermore, we apply this sampling strategy to sample
global input crops from the source and target dataset. In addition, we propose
feature-attentive denormalization to selectively incorporate content-based
statistics into the generator stream. In our experiments, we show that our
method achieves state-of-the-art performance in photorealistic sim-to-real
translation and weather translation and also performs well in day-to-night
translation. Additionally, we propose the cKVD metric, which builds on the sKVD
metric and enables the examination of translation quality at the class or
category level.