  State-of-the-art deep neural network (DNN) pruning techniques, applied
one-shot before training starts, evaluate sparse architectures with the help of
a single criterion -- called pruning score. Pruning weights based on a solitary
score works well for some architectures and pruning rates but may also fail for
other ones. As a common baseline for pruning scores, we introduce the notion of
a generalized synaptic score (GSS). In this work we do not concentrate on a
single pruning criterion, but provide a framework for combining arbitrary GSSs
to create more powerful pruning strategies. These COmbined Pruning Scores
(COPS) are obtained by solving a constrained optimization problem. Optimizing
for more than one score prevents the sparse network to overly specialize on an
individual task, thus COntrols Pruning before training Starts. The
combinatorial optimization problem given by COPS is relaxed on a linear program
(LP). This LP is solved analytically and determines a solution for COPS.
Furthermore, an algorithm to compute it for two scores numerically is proposed
and evaluated. Solving COPS in such a way has lower complexity than the best
general LP solver. In our experiments we compared pruning with COPS against
state-of-the-art methods for different network architectures and image
classification tasks and obtained improved results.
