Discriminative reconstruction via simultaneous dense and sparse coding

Understand

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.
• The model studies the problem of recovering a dense vector $\mathbf{x}$ and a sparse vector $\mathbf{u}$ given measurements of the form $\mathbf{y} = \mathbf{A}\mathbf{x}+\mathbf{B}\mathbf{u}$.