  We propose an efficient algorithm for the generalized sparse coding (SC)
inference problem. The proposed framework applies to both the single dictionary
setting, where each data point is represented as a sparse combination of the
columns of one dictionary matrix, as well as the multiple dictionary setting as
given in morphological component analysis (MCA), where the goal is to separate
a signal into additive parts such that each part has distinct sparse
representation within a corresponding dictionary. Both the SC task and its
generalization via MCA have been cast as $\ell_1$-regularized least-squares
optimization problems. To accelerate traditional acquisition of sparse codes,
we propose a deep learning architecture that constitutes a trainable
time-unfolded version of the Split Augmented Lagrangian Shrinkage Algorithm
(SALSA), a special case of the Alternating Direction Method of Multipliers
(ADMM). We empirically validate both variants of the algorithm, that we refer
to as LSALSA (learned-SALSA), on image vision tasks and demonstrate that at
inference our networks achieve vast improvements in terms of the running time,
the quality of estimated sparse codes, and visual clarity on both classic SC
and MCA problems. Finally, we present a theoretical framework for analyzing
LSALSA network: we show that the proposed approach exactly implements a
truncated ADMM applied to a new, learned cost function with curvature modified
by one of the learned parameterized matrices. We extend a very recent
Stochastic Alternating Optimization analysis framework to show that a gradient
descent step along this learned loss landscape is equivalent to a modified
gradient descent step along the original loss landscape. In this framework, the
acceleration achieved by LSALSA could potentially be explained by the network's
ability to learn a correction to the gradient direction of steeper descent.
