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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 3: AAAI-21 Technical Tracks 3

DPFPS: Dynamic and Progressive Filter Pruning for Compressing Convolutional Neural Networks from Scratch

February 1, 2023

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Authors

Xiaofeng Ruan

National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences


Yufan Liu

National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences


Bing Li

National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences PeopleAI Inc.


Chunfeng Yuan

National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences


Weiming Hu

National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology


DOI:

10.1609/aaai.v35i3.16351


Abstract:

Filter pruning is a commonly used method for compressing Convolutional Neural Networks (ConvNets), due to its friendly hardware supporting and flexibility. However, existing methods mostly need a cumbersome procedure, which brings many extra hyper-parameters and training epochs. This is because only using sparsity and pruning stages cannot obtain a satisfying performance. Besides, many works do not consider the difference of pruning ratio across different layers. To overcome these limitations, we propose a novel dynamic and progressive filter pruning (DPFPS) scheme that directly learns a structured sparsity network from Scratch. In particular, DPFPS imposes a new structured sparsity-inducing regularization specifically upon the expected pruning parameters in a dynamic sparsity manner. The dynamic sparsity scheme determines sparsity allocation ratios of different layers and a Taylor series based channel sensitivity criteria is presented to identify the expected pruning parameters. Moreover, we increase the structured sparsity-inducing penalty in a progressive manner. This helps the model to be sparse gradually instead of forcing the model to be sparse at the beginning. Our method solves the pruning ratio based optimization problem by an iterative soft-thresholding algorithm (ISTA) with dynamic sparsity. At the end of the training, we only need to remove the redundant parameters without other stages, such as fine-tuning. Extensive experimental results show that the proposed method is competitive with 11 state-of-the-art methods on both small-scale and large-scale datasets (i.e., CIFAR and ImageNet). Specifically, on ImageNet, we achieve a 44.97% pruning ratio of FLOPs by compressing ResNet-101, even with an increase of 0.12% Top-5 accuracy. Our pruned models and codes are released at https://github.com/taoxvzi/DPFPS.

Topics: AAAI

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HOW TO CITE:

Xiaofeng Ruan||Yufan Liu||Bing Li||Chunfeng Yuan||Weiming Hu DPFPS: Dynamic and Progressive Filter Pruning for Compressing Convolutional Neural Networks from Scratch Proceedings of the AAAI Conference on Artificial Intelligence (2021) 2495-2503.

Xiaofeng Ruan||Yufan Liu||Bing Li||Chunfeng Yuan||Weiming Hu DPFPS: Dynamic and Progressive Filter Pruning for Compressing Convolutional Neural Networks from Scratch AAAI 2021, 2495-2503.

Xiaofeng Ruan||Yufan Liu||Bing Li||Chunfeng Yuan||Weiming Hu (2021). DPFPS: Dynamic and Progressive Filter Pruning for Compressing Convolutional Neural Networks from Scratch. Proceedings of the AAAI Conference on Artificial Intelligence, 2495-2503.

Xiaofeng Ruan||Yufan Liu||Bing Li||Chunfeng Yuan||Weiming Hu. DPFPS: Dynamic and Progressive Filter Pruning for Compressing Convolutional Neural Networks from Scratch. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.2495-2503.

Xiaofeng Ruan||Yufan Liu||Bing Li||Chunfeng Yuan||Weiming Hu. 2021. DPFPS: Dynamic and Progressive Filter Pruning for Compressing Convolutional Neural Networks from Scratch. "Proceedings of the AAAI Conference on Artificial Intelligence". 2495-2503.

Xiaofeng Ruan||Yufan Liu||Bing Li||Chunfeng Yuan||Weiming Hu. (2021) "DPFPS: Dynamic and Progressive Filter Pruning for Compressing Convolutional Neural Networks from Scratch", Proceedings of the AAAI Conference on Artificial Intelligence, p.2495-2503

Xiaofeng Ruan||Yufan Liu||Bing Li||Chunfeng Yuan||Weiming Hu, "DPFPS: Dynamic and Progressive Filter Pruning for Compressing Convolutional Neural Networks from Scratch", AAAI, p.2495-2503, 2021.

Xiaofeng Ruan||Yufan Liu||Bing Li||Chunfeng Yuan||Weiming Hu. "DPFPS: Dynamic and Progressive Filter Pruning for Compressing Convolutional Neural Networks from Scratch". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.2495-2503.

Xiaofeng Ruan||Yufan Liu||Bing Li||Chunfeng Yuan||Weiming Hu. "DPFPS: Dynamic and Progressive Filter Pruning for Compressing Convolutional Neural Networks from Scratch". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 2495-2503.

Xiaofeng Ruan||Yufan Liu||Bing Li||Chunfeng Yuan||Weiming Hu. DPFPS: Dynamic and Progressive Filter Pruning for Compressing Convolutional Neural Networks from Scratch. AAAI[Internet]. 2021[cited 2023]; 2495-2503.


ISSN: 2374-3468


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