@inproceedings{
xie2024adaptive,
title={Adaptive Pruning of Channel Spatial Dependability in Convolutional Neural Networks},
author={Weiying Xie and Mei Yuan and Jitao Ma and Yunsong Li},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=YFOjMPQkJc}
}
Deep Convolutional Neural Networks (CNNs) have demonstrated excellent performance in various multimedia application scenarios. However, complex models often require significant computational resources and energy costs. Therefore, CNN compression is crucial for addressing deployment challenges of multimedia application on resource constrained edge devices. However, existing CNN channel pruning strategies primarily focus on the "weights" or "activations" of the model, overlooking its "interpretability" information. In this paper, we explore CNN pruning strategies from the perspective of model interpretability. We model the correspondence between channel feature maps and interpretable visual perception based on class saliency maps, aiming to assess the contribution of each channel to the desired output. Additionally, we utilize Discrete Wavelet Transform (DWT) to capture the global features and structure of class saliency maps. Based on this, we propose a Channel Spatial Dependability (CSD) metric, evaluating the importance and contribution of channels in a bidirectional manner to guide model quantization pruning. And we dynamically adjust the pruning rate of each layer based on performance changes, in order to achieve more accurate and efficient adaptive pruning. Experimental results demonstrate that our method achieves significant results across a range of different networks and datasets. For instance, we achieved a 51.3% pruning on the ResNet-56 model while maintaining an accuracy of 94.16%, outperforming feature-map or weight-based pruning and other State-of-the-Art (SOTA).
Adaptive Pruning of Channel Spatial Dependability in Convolutional Neural Networks
Weiying Xie Mei Yuan Jitao Ma Yunsong Li