Neural networks grow vastly in size to tackle more sophisticated tasks. In
many cases, such large networks are not deployable on particular hardware and
need to be reduced in size. Pruning techniques help to shrink deep neural
networks to smaller sizes by only decreasing their performance as little as
possible. However, such pruning algorithms are often hard to understand by
applying them and do not include domain knowledge which can potentially be bad
for user goals. We propose ViNNPruner, a visual interactive pruning application
that implements state-of-the-art pruning algorithms and the option for users to
do manual pruning based on their knowledge. We show how the application
facilitates gaining insights into automatic pruning algorithms and
semi-automatically pruning oversized networks to make them more efficient using
interactive visualizations.Comment: MLVis Short Paper; 4 pages, 1 page references, 3 figure