  Although multi-task deep neural network (DNN) models have computation and
storage benefits over individual single-task DNN models, they can be further
optimized via model compression. Numerous structured pruning methods are
already developed that can readily achieve speedups in single-task models, but
the pruning of multi-task networks has not yet been extensively studied. In
this work, we investigate the effectiveness of structured pruning on multi-task
models. We use an existing single-task filter pruning criterion and also
introduce an MTL-based filter pruning criterion for estimating the filter
importance scores. We prune the model using an iterative pruning strategy with
both pruning methods. We show that, with careful hyper-parameter tuning,
architectures obtained from different pruning methods do not have significant
differences in their performances across tasks when the number of parameters is
similar. We also show that iterative structure pruning may not be the best way
to achieve a well-performing pruned model because, at extreme pruning levels,
there is a high drop in performance across all tasks. But when the same models
are randomly initialized and re-trained, they show better results.
