  Models trained on datasets with texture bias usually perform poorly on
out-of-distribution samples since biased representations are embedded into the
model. Recently, various image translation and debiasing methods have attempted
to disentangle texture biased representations for downstream tasks, but
accurately discarding biased features without altering other relevant
information is still challenging. In this paper, we propose a novel framework
that leverages image translation to generate additional training images using
the content of a source image and the texture of a target image with a
different bias property to explicitly mitigate texture bias when training a
model on a target task. Our model ensures texture similarity between the target
and generated images via a texture co-occurrence loss while preserving content
details from source images with a spatial self-similarity loss. Both the
generated and original training images are combined to train improved
classification or segmentation models robust to inconsistent texture bias.
Evaluation on five classification- and two segmentation-datasets with known
texture biases demonstrates the utility of our method, and reports significant
improvements over recent state-of-the-art methods in all cases.
