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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 1: AAAI-22 Technical Tracks 1

Prior Gradient Mask Guided Pruning-Aware Fine-Tuning

February 1, 2023

Authors

Linhang Cai

Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China


Zhulin An

Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China


Chuanguang Yang

Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China


Yangchun Yan

Horizon Robotics Inc, Beijing, China


Yongjun Xu

Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China


Proceedings:

No. 1: AAAI-22 Technical Tracks 1

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Computer Vision I

Downloads:

Download PDF

Abstract:

We proposed a Prior Gradient Mask Guided Pruning-aware Fine-Tuning (PGMPF) framework to accelerate deep Convolutional Neural Networks (CNNs). In detail, the proposed PGMPF selectively suppresses the gradient of those ”unimportant” parameters via a prior gradient mask generated by the pruning criterion during fine-tuning. PGMPF has three charming characteristics over previous works: (1) Pruning-aware network fine-tuning. A typical pruning pipeline consists of training, pruning and fine-tuning, which are relatively independent, while PGMPF utilizes a variant of the pruning mask as a prior gradient mask to guide fine-tuning, without complicated pruning criteria. (2) An excellent tradeoff between large model capacity during fine-tuning and stable convergence speed to obtain the final compact model. Previous works preserve more training information of pruned parameters during fine-tuning to pursue better performance, which would incur catastrophic non-convergence of the pruned model for relatively large pruning rates, while our PGMPF greatly stabilizes the fine-tuning phase by gradually constraining the learning rate of those ”unimportant” parameters. (3) Channel-wise random dropout of the prior gradient mask to impose some gradient noise to fine-tuning to further improve the robustness of final compact model. Experimental results on three image classification benchmarks CIFAR10/ 100 and ILSVRC-2012 demonstrate the effectiveness of our method for various CNN architectures, datasets and pruning rates. Notably, on ILSVRC-2012, PGMPF reduces 53.5% FLOPs on ResNet-50 with only 0.90% top-1 accuracy drop and 0.52% top-5 accuracy drop, which has advanced the state-of-the-art with negligible extra computational cost.

DOI:

10.1609/aaai.v36i1.19888


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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