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Models trained on datasets with texture bias usually perform poorly onout-of-distribution samples since biased representations are embedded into themodel. Recently, various image translation and debiasing methods have attemptedto disentangle texture biased representations for downstream tasks, butaccurately discarding biased features without altering other relevantinformation is still challenging. In this paper, we propose a novel frameworkthat leverages image translation to generate additional training images usingthe content of a source image and the texture of a target image with adifferent bias property to explicitly mitigate texture bias when training amodel on a target task. Our model ensures texture similarity between the targetand generated images via a texture co-occurrence loss while preserving contentdetails from source images with a spatial self-similarity loss. Both thegenerated and original training images are combined to train improvedclassification or segmentation models robust to inconsistent texture bias.Evaluation on five classification- and two segmentation-datasets with knowntexture biases demonstrates the utility of our method, and reports significantimprovements over recent state-of-the-art methods in all cases.

Content Preserving Image Translation with Texture Co-occurrence and Spatial Self-Similarity for Texture Debiasing and Domain Adaptation