  Today's deep neural networks require substantial computation resources for
their training, storage, and inference, which limits their effective use on
resource-constrained devices. Many recent research activities explore different
options for compressing and optimizing deep models. On the one hand, in many
real-world applications, we face the data imbalance challenge, i.e. when the
number of labeled instances of one class considerably outweighs the number of
labeled instances of the other class. On the other hand, applications may pose
a class imbalance problem, i.e. higher number of false positives produced when
training a model and optimizing its performance may be tolerable, yet the
number of false negatives must stay low. The problem originates from the fact
that some classes are more important for the application than others, e.g.
detection problems in medical and surveillance domains. Motivated by the
success of the lottery ticket hypothesis, in this paper we propose an iterative
deep model compression technique, which keeps the number of false negatives of
the compressed model close to the one of the original model at the price of
increasing the number of false positives if necessary. Our experimental
evaluation using two benchmark data sets shows that the resulting compressed
sub-networks 1) achieve up to 35% lower number of false negatives than the
compressed model without class optimization, 2) provide an overall higher
AUC_ROC measure, and 3) use up to 99% fewer parameters compared to the original
network.
