Convolutional Neural Networks (CNNs) compression is crucial to deploying
these models in edge devices with limited resources. Existing channel pruning
algorithms for CNNs have achieved plenty of success on complex models. They
approach the pruning problem from various perspectives and use different
metrics to guide the pruning process. However, these metrics mainly focus on
the model's `outputs' or `weights' and neglect its `interpretations'
information. To fill in this gap, we propose to address the channel pruning
problem from a novel perspective by leveraging the interpretations of a model
to steer the pruning process, thereby utilizing information from both inputs
and outputs of the model. However, existing interpretation methods cannot get
deployed to achieve our goal as either they are inefficient for pruning or may
predict non-coherent explanations. We tackle this challenge by introducing a
selector model that predicts real-time smooth saliency masks for pruned models.
We parameterize the distribution of explanatory masks by Radial Basis Function
(RBF)-like functions to incorporate geometric prior of natural images in our
selector model's inductive bias. Thus, we can obtain compact representations of
explanations to reduce the computational costs of our pruning method. We
leverage our selector model to steer the network pruning by maximizing the
similarity of explanatory representations for the pruned and original models.
Extensive experiments on CIFAR-10 and ImageNet benchmark datasets demonstrate
the efficacy of our proposed method. Our implementations are available at
https://github.com/Alii-Ganjj/InterpretationsSteeredPruning

本文提出一种基于模型解释的方式对 CNNs 进行精简，在引入一个实时平滑的选择模型以及径向基函数（RBF）等经典几何先验和解释模型的参数化的方式下，通过最大化解释模型的相似性来引导网络裁剪，实现了对性能、精简度和解释一些重要指标的平衡。