ABCP: Automatic Block-wise and Channel-wise Network Pruning via Joint Search
Jiaqi Li, Haoran Li, Yaran Chen, Zixiang Ding, Nannan Li, Mingjun Ma,, Zicheng Duan, and Dongbing Zhao

TL;DR
ABCP uses deep reinforcement learning to automatically perform block-wise and channel-wise network pruning, achieving high compression ratios with minimal accuracy loss, reducing manual effort and improving robustness.
Contribution
It introduces a joint search method for block-wise and channel-wise pruning using reinforcement learning, outperforming traditional rule-based approaches.
Findings
Achieves 99.5% FLOPs reduction on YOLOv3
Reduces parameters by 99.5% with 2.8% mAP loss
Enhances robustness in transfer detection tasks
Abstract
Currently, an increasing number of model pruning methods are proposed to resolve the contradictions between the computer powers required by the deep learning models and the resource-constrained devices. However, most of the traditional rule-based network pruning methods can not reach a sufficient compression ratio with low accuracy loss and are time-consuming as well as laborious. In this paper, we propose Automatic Block-wise and Channel-wise Network Pruning (ABCP) to jointly search the block-wise and channel-wise pruning action with deep reinforcement learning. A joint sample algorithm is proposed to simultaneously generate the pruning choice of each residual block and the channel pruning ratio of each convolutional layer from the discrete and continuous search space respectively. The best pruning action taking both the accuracy and the complexity of the model into account is obtained…
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Taxonomy
TopicsAdvanced Neural Network Applications · Domain Adaptation and Few-Shot Learning · Multimodal Machine Learning Applications
MethodsPruning · Average Pooling · Softmax · Global Average Pooling · 1x1 Convolution · Residual Connection · Batch Normalization · Convolution · *Communicated@Fast*How Do I Communicate to Expedia? · BNB Customer Service Number +1-833-534-1729
