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In machine learning and computer vision, input images are often filtered toincrease data discriminability. In some situations, however, one may wish topurposely decrease discriminability of one classification task (a "distractor"task), while simultaneously preserving information relevant to another (thetask-of-interest): For example, it may be important to mask the identity ofpersons contained in face images before submitting them to a crowdsourcing site(e.g., Mechanical Turk) when labeling them for certain facial attributes.Another example is inter-dataset generalization: when training on a datasetwith a particular covariance structure among multiple attributes, it may beuseful to suppress one attribute while preserving another so that a trainedclassifier does not learn spurious correlations between attributes. In thispaper we present an algorithm that finds optimal filters to give highdiscriminability to one task while simultaneously giving low discriminabilityto a distractor task. We present results showing the effectiveness of theproposed technique on both simulated data and natural face images.

Discriminately Decreasing Discriminability with Learned Image Filters