Learning with Privileged Information for Improved Target Classification

摘要

This work considers "Learning Using Privileged Information" (LUPI) paradigm. LUPI improves classification accuracy by incorporating additional information available at training time and not available during testing. In this contribution, the LUPI paradigm is tested on a Wide Area Motion Imagery (WAMI) dataset and on images from the Caltech 101 dataset. In both cases a consistent improvement in classification accuracy is observed. The results are discussed and the directions of future research are outlined.