AutoDFP: Automatic Data-Free Pruning via Channel Similarity
  Reconstruction
Siqi Li Jun Chen Jingyang Xiang Chengrui Zhu Yong Liu
Structured pruning methods are developed to bridge the gap between the
massive scale of neural networks and the limited hardware resources. Most
current structured pruning methods rely on training datasets to fine-tune the
compressed model, resulting in high computational burdens and being
inapplicable for scenarios with stringent requirements on privacy and security.
As an alternative, some data-free methods have been proposed, however, these
methods often require handcraft parameter tuning and can only achieve
inflexible reconstruction. In this paper, we propose the Automatic Data-Free
Pruning (AutoDFP) method that achieves automatic pruning and reconstruction
without fine-tuning. Our approach is based on the assumption that the loss of
information can be partially compensated by retaining focused information from
similar channels. Specifically, We formulate data-free pruning as an
optimization problem, which can be effectively addressed through reinforcement
learning. AutoDFP assesses the similarity of channels for each layer and
provides this information to the reinforcement learning agent, guiding the
pruning and reconstruction process of the network. We evaluate AutoDFP with
multiple networks on multiple datasets, achieving impressive compression
results. For instance, on the CIFAR-10 dataset, AutoDFP demonstrates a 2.87\%
reduction in accuracy loss compared to the recently proposed data-free pruning
method DFPC with fewer FLOPs on VGG-16. Furthermore, on the ImageNet dataset,
AutoDFP achieves 43.17\% higher accuracy than the SOTA method with the same
80\% preserved ratio on MobileNet-V1.