  In machine learning and computer vision, input images are often filtered to
increase data discriminability. In some situations, however, one may wish to
purposely decrease discriminability of one classification task (a "distractor"
task), while simultaneously preserving information relevant to another (the
task-of-interest): For example, it may be important to mask the identity of
persons 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 dataset
with a particular covariance structure among multiple attributes, it may be
useful to suppress one attribute while preserving another so that a trained
classifier does not learn spurious correlations between attributes. In this
paper we present an algorithm that finds optimal filters to give high
discriminability to one task while simultaneously giving low discriminability
to a distractor task. We present results showing the effectiveness of the
proposed technique on both simulated data and natural face images.
