Monday, September 26
12.10 - 1.00
CSE 4140

Speaker: Jacob Whitehill, UCSD
Title: Discriminately Decreasing Discriminability with Learned Image Filters
Abstract:
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. The algorithm is flexible and can be applied in either the space or frequency domain. We present results showing the effectiveness of the proposed technique on both simulated data and natural face images.