  The rapid development of large-scale deep learning models questions the
affordability of hardware platforms, which necessitates the pruning to reduce
their computational and memory footprints. Sparse neural networks as the
product, have demonstrated numerous favorable benefits like low complexity,
undamaged generalization, etc. Most of the prominent pruning strategies are
invented from a model-centric perspective, focusing on searching and preserving
crucial weights by analyzing network topologies. However, the role of data and
its interplay with model-centric pruning has remained relatively unexplored. In
this research, we introduce a novel data-model co-design perspective: to
promote superior weight sparsity by learning important model topology and
adequate input data in a synergetic manner. Specifically, customized Visual
Prompts are mounted to upgrade neural Network sparsification in our proposed
VPNs framework. As a pioneering effort, this paper conducts systematic
investigations about the impact of different visual prompts on model pruning
and suggests an effective joint optimization approach. Extensive experiments
with 3 network architectures and 8 datasets evidence the substantial
performance improvements from VPNs over existing start-of-the-art pruning
algorithms. Furthermore, we find that subnetworks discovered by VPNs from
pre-trained models enjoy better transferability across diverse downstream
scenarios. These insights shed light on new promising possibilities of
data-model co-designs for vision model sparsification.
