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Many Computer Vision problems arise from information processing of data sources with nuisance variances like scale, orientation, contrast, perspective foreshortening orin medical imaging -staining and local warping. In most cases these variances can be stated a priori and can be used to improve the generalization of recognition algorithms. We propose a novel supervised feature learning approach, which efficiently extracts information from these constraints to produce interpretable,doi:10.1109/cvpr.2015.7298923 dblp:conf/cvpr/LaptevB15 fatcat:jkh6nmtiffas5ji6rzcfef2gim