Flexible Automatic Identification and Removal (FAIR)-Pruner: An Efficient Neural Network Pruning Method

• URL: http://arxiv.org/abs/2508.02291v1
• Date: Mon, 04 Aug 2025 10:59:07 GMT
• Title: Flexible Automatic Identification and Removal (FAIR)-Pruner: An Efficient Neural Network Pruning Method
• Authors: Chenqing Lin, Mostafa Hussien, Chengyao Yu, Mohamed Cheriet, Osama Abdelrahman, Ruixing Ming,
• Abstract summary: This paper proposes the Flexible Automatic Identification and Removal (FAIR)-Pruner, a novel method for neural network structured pruning.<n>A major advantage of FAIR-Pruner lies in its capacity to automatically determine the layer-wise pruning rates, which yields a more efficient subnetwork structure.<n>With utilization scores and reconstruction errors, users can flexibly obtain pruned models under different pruning ratios.
• Score: 11.575879702610914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural network pruning is a critical compression technique that facilitates the deployment of large-scale neural networks on resource-constrained edge devices, typically by identifying and eliminating redundant or insignificant parameters to reduce computational and memory overhead. This paper proposes the Flexible Automatic Identification and Removal (FAIR)-Pruner, a novel method for neural network structured pruning. Specifically, FAIR-Pruner first evaluates the importance of each unit (e.g., neuron or channel) through the Utilization Score quantified by the Wasserstein distance. To reflect the performance degradation after unit removal, it then introduces the Reconstruction Error, which is computed via the Taylor expansion of the loss function. Finally, FAIR-Pruner identifies superfluous units with negligible impact on model performance by controlling the proposed Tolerance of Difference, which measures differences between unimportant units and those that cause performance degradation. A major advantage of FAIR-Pruner lies in its capacity to automatically determine the layer-wise pruning rates, which yields a more efficient subnetwork structure compared to applying a uniform pruning rate. Another advantage of the FAIR-Pruner is its great one-shot performance without post-pruning fine-tuning. Furthermore, with utilization scores and reconstruction errors, users can flexibly obtain pruned models under different pruning ratios. Comprehensive experimental validation on diverse benchmark datasets (e.g., ImageNet) and various neural network architectures (e.g., VGG) demonstrates that FAIR-Pruner achieves significant model compression while maintaining high accuracy.

Related papers

• RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration [0.0]
  We propose RL-Pruner, which uses reinforcement learning to learn the optimal pruning distribution. RL-Pruner can automatically extract dependencies between filters in the input model and perform pruning, without requiring model-specific pruning implementations.
  arXiv  Detail & Related papers  (2024-11-10T13:35:10Z)
• MPruner: Optimizing Neural Network Size with CKA-Based Mutual Information Pruning [7.262751938473306]
  Pruning is a well-established technique that reduces the size of neural networks while mathematically guaranteeing accuracy preservation. We develop a new pruning algorithm, MPruner, that leverages mutual information through vector similarity. MPruner achieved up to a 50% reduction in parameters and memory usage for CNN and transformer-based models, with minimal to no loss in accuracy.
  arXiv  Detail & Related papers  (2024-08-24T05:54:47Z)
• Neural Network Pruning for Real-time Polyp Segmentation [8.08470060885395]
  We show an application of neural network pruning in polyp segmentation. We compute the importance score of convolutional filters and remove the filters having the least scores, which to some value of pruning does not degrade the performance.
  arXiv  Detail & Related papers  (2023-06-22T21:03:50Z)
• Differentiable Neural Networks with RePU Activation: with Applications to Score Estimation and Isotonic Regression [7.450181695527364]
  We study the properties of differentiable neural networks activated by rectified power unit (RePU) functions. We establish error bounds for simultaneously approximating $Cs$ smooth functions and their derivatives using RePU-activated deep neural networks.
  arXiv  Detail & Related papers  (2023-05-01T00:09:48Z)
• Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution [91.3781512926942]
  Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. This work investigates the potential of network pruning for super-resolution iteration to take advantage of off-the-shelf network designs and reduce the underlying computational overhead. We propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly network at each and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly.
  arXiv  Detail & Related papers  (2023-03-16T21:06:13Z)
• A Fair Loss Function for Network Pruning [70.35230425589592]
  We introduce the performance weighted loss function, a simple modified cross-entropy loss function that can be used to limit the introduction of biases during pruning. Experiments using the CelebA, Fitzpatrick17k and CIFAR-10 datasets demonstrate that the proposed method is a simple and effective tool.
  arXiv  Detail & Related papers  (2022-11-18T15:17:28Z)
• Self-Compression in Bayesian Neural Networks [0.9176056742068814]
  We propose a new insight into network compression through the Bayesian framework. We show that Bayesian neural networks automatically discover redundancy in model parameters, thus enabling self-compression. Our experimental results show that the network architecture can be successfully compressed by deleting parameters identified by the network itself.
  arXiv  Detail & Related papers  (2021-11-10T21:19:40Z)
• Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities [80.06422693778141]
  We propose a flexible new pruning mechanism that facilitates pruning at different granularities (weights, kernels, filters/feature maps) We refer to this algorithm as Dynamic Probabilistic Pruning (DPP) We show that DPP achieves competitive compression rates and classification accuracy when pruning common deep learning models trained on different benchmark datasets for image classification.
  arXiv  Detail & Related papers  (2021-05-26T17:01:52Z)
• Manifold Regularized Dynamic Network Pruning [102.24146031250034]
  This paper proposes a new paradigm that dynamically removes redundant filters by embedding the manifold information of all instances into the space of pruned networks. The effectiveness of the proposed method is verified on several benchmarks, which shows better performance in terms of both accuracy and computational cost.
  arXiv  Detail & Related papers  (2021-03-10T03:59:03Z)
• DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator Search [55.164053971213576]
  convolutional neural network has achieved great success in fulfilling computer vision tasks despite large computation overhead. Structured (channel) pruning is usually applied to reduce the model redundancy while preserving the network structure. Existing structured pruning methods require hand-crafted rules which may lead to tremendous pruning space.
  arXiv  Detail & Related papers  (2020-11-04T07:43:01Z)
• DHP: Differentiable Meta Pruning via HyperNetworks [158.69345612783198]
  This paper introduces a differentiable pruning method via hypernetworks for automatic network pruning. Latent vectors control the output channels of the convolutional layers in the backbone network and act as a handle for the pruning of the layers. Experiments are conducted on various networks for image classification, single image super-resolution, and denoising.
  arXiv  Detail & Related papers  (2020-03-30T17:59:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.