"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."

"The conclusion is as unpleasant as it is inevitable. The copyright industry lobby is actively trying to hide egregious crimes against children, obviously not because they care about the children, but because the resulting censorship mechanism can be a benefit to their business if they manage to broaden the censorship in the next stage. All this in defense of their lucrative monopoly that starves the public of culture."

"I have accordingly been consulting with Casey on how to remove all the butt-shots from the Yale University MARC records."