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.

本研究从数据 - 模型协同设计的角度，提出了一种新颖的数据 - 模型协同设计方法，通过学习重要的模型拓扑和充足的输入数据，促进优秀权重稀疏性。实验结果表明，使用该方法可以显著提高模型剪枝效果，并且从预训练模型中发现的子网络在不同的场景下具有更好的迁移性能，为视觉模型稀疏化的数据 - 模型协同设计提供了新的可能性。