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Singular values-driven automated filter pruning.

Van Tien Pham1, Yassine Zniyed1, Thanh Phuong Nguyen2

  • 1Université de Toulon, Aix Marseille University, CNRS, LIS, UMR 7020, France.

Neural Networks : the Official Journal of the International Neural Network Society
|July 20, 2025
PubMed
Summary
This summary is machine-generated.

We introduce SLIMING, an automated filter pruning method using singular values to optimize neural network filters. This approach simplifies complex pruning into two steps, preserving filter structure for improved efficiency.

Keywords:
Automated pruningHOSVDMatrix and tensor decompositionsNetwork compressionStructured pruning

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Machine Learning

Background:

  • Automated filter pruning is crucial for efficient deep learning models.
  • Existing methods face challenges in optimizing filter tensors and preserving multidimensional structures.

Purpose of the Study:

  • To present SLIMING (Singular vaLues-drIven autoMated filter prunING), a novel automated filter pruning method.
  • To address the combinatorial challenge in filter pruning by proposing a two-step optimization process.

Main Methods:

  • SLIMING formalizes filter pruning as an optimization problem using singular values.
  • A two-step approach is proposed: (i) determining pruning configuration and (ii) selecting filters.
  • Straightforward algorithms are developed for each step, ensuring preservation of filter multidimensional structure.

Main Results:

  • Numerical simulations on a synthetic toy example validated the approach.
  • Extensive simulations across eight architectures, four datasets, and four vision tasks demonstrated the framework's efficacy.

Conclusions:

  • SLIMING offers an effective and structured approach to automated filter pruning.
  • The method successfully preserves filter multidimensionality, enhancing model efficiency and performance.