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Gated filter pruning via sample manifold relationships

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Abstract

Filter pruning is an essential method for compressing and accelerating deep neural networks on computationally restricted devices. Despite recognizing the high correlation between filter redundancy and samples, existing methods primarily focus on independently searching for optimal subnetworks from individual input while ignoring the relationships among different inputs. In this paper, we propose a novel approach called Gated Filter Pruning based on Sample Manifold Relationships, which exploits and aligns the manifold relationships of all samples during training to obtain an optimal subnetwork. Firstly, we introduce a Gated Filter Normalization Module (GFNM) that excavates the manifold information of each sample, applicable to the operator level without adding many additional parameters. GFNM incorporates explainable control variables jointly optimized with convolutional weights, explicitly determining the competition and cooperation among filters during training. Subsequently, Manifold Regularized Pruning Module (MRPM) measures the manifold relationships between samples and subnetworks, efficiently regularizing the solution space of sample-network pairs. The manifold relationships between samples and subnetworks are aligned in training to derive an effective subnetwork for all input samples. Extensive experimental results validate the effectiveness of our method, demonstrating competitive performance in terms of accuracy and computational cost compared to state-of-the-art (SOTA) methods.

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Data Availability

The data and codes generated during and analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors gratefully acknowledge support from the Natural Science Foundation of Jiangsu Province of China (BK20222012), Guangxi Science and Technology Project AB22080026/2021AB22167, National Natural Science Foundation of China (No. 61375021). Thanks to the editor and anonymous reviewers for their valuable comments and suggestions.

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Pingfan Wu, Hengyi Huang, Han Sun & Ningzhong Liu

  2. MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, 211106, China

    Pingfan Wu, Hengyi Huang & Ningzhong Liu

  3. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, 211106, China

    Pingfan Wu, Hengyi Huang & Ningzhong Liu

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  1. Pingfan Wu
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  2. Hengyi Huang
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  3. Han Sun
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  4. Ningzhong Liu
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Contributions

Pingfan Wu: Conceptualization, Methodology, Validation, Visualization, Formal analysis, Investigation, Writing - original draft. Hengyi Huang: Writing - review editing, Supervision. Ningzhong Liu: Writing - review editing, Project administration, Investigation. Han Sun: Writing - review editing, Supervision.

Corresponding author

Correspondence to Ningzhong Liu.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. The authors declared that the research do not involving Human Participants and Animals. Written informed consent was obtained from all the participants prior to the enrollment (or for the publication) of this study.

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Wu, P., Huang, H., Sun, H. et al. Gated filter pruning via sample manifold relationships. Appl Intell 54, 9848–9863 (2024). https://doi.org/10.1007/s10489-024-05690-w

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