Self-Adaptive Network Pruning
This addresses the problem of computational inefficiency in CNNs for industrial applications, representing an incremental improvement over existing pruning methods.
The paper tackles the high computational cost of deep convolutional neural networks by proposing a self-adaptive network pruning method (SANP) that dynamically adjusts pruning per layer and sample to meet a budget, achieving superior classification accuracy and pruning rates in experiments on 2 datasets and 3 backbones.
Deep convolutional neural networks have been proved successful on a wide range of tasks, yet they are still hindered by their large computation cost in many industrial scenarios. In this paper, we propose to reduce such cost for CNNs through a self-adaptive network pruning method (SANP). Our method introduces a general Saliency-and-Pruning Module (SPM) for each convolutional layer, which learns to predict saliency scores and applies pruning for each channel. Given a total computation budget, SANP adaptively determines the pruning strategy with respect to each layer and each sample, such that the average computation cost meets the budget. This design allows SANP to be more efficient in computation, as well as more robust to datasets and backbones. Extensive experiments on 2 datasets and 3 backbones show that SANP surpasses state-of-the-art methods in both classification accuracy and pruning rate.