Discriminative reconstruction via simultaneous dense and sparse coding

ArXi:2006.09534v4 Announce Type: replace-cross Discriminative features extracted from the sparse coding model have been shown to perform well for classification. Recent deep learning architectures have further improved reconstruction in inverse problems by considering new dense priors learned from data. We propose a novel dense and sparse coding model that integrates both representation capability and discriminative features.