LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation

• URL: http://arxiv.org/abs/2507.21573v1
• Date: Tue, 29 Jul 2025 08:17:10 GMT
• Title: LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation
• Authors: Maxim Henry, Adrien Deliège, Anthony Cioppa, Marc Van Droogenbroeck,
• Abstract summary: Convolutional Neural Networks (CNN) are widely used in many computer vision tasks. Yet, their increasing size and complexity pose significant challenges for efficient deployment on resource-constrained platforms.<n>We introduce LinDeps, a novel post-pruning method that can be applied on top of any pruning technique.<n>Our experiments on CIFAR-10 and ImageNet with VGG and ResNet backbones demonstrate that LinDeps improves compression rates of existing pruning techniques while preserving performances.
• Score: 11.693806647824532
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolutional Neural Networks (CNN) are widely used in many computer vision tasks. Yet, their increasing size and complexity pose significant challenges for efficient deployment on resource-constrained platforms. Hence, network pruning has emerged as an effective way of reducing the size and computational requirements of neural networks by removing redundant or unimportant parameters. However, a fundamental challenge with pruning consists in optimally removing redundancies without degrading performance. Most existing pruning techniques overlook structural dependencies across feature maps within a layer, resulting in suboptimal pruning decisions. In this work, we introduce LinDeps, a novel post-pruning method, i.e., a pruning method that can be applied on top of any pruning technique, which systematically identifies and removes redundant filters via linear dependency analysis. Particularly, LinDeps applies pivoted QR decomposition to feature maps to detect and prune linearly dependent filters. Then, a novel signal recovery mechanism adjusts the next layer's kernels to preserve compatibility and performance without requiring any fine-tuning. Our experiments on CIFAR-10 and ImageNet with VGG and ResNet backbones demonstrate that LinDeps improves compression rates of existing pruning techniques while preserving performances, leading to a new state of the art in CNN pruning. We also benchmark LinDeps in low-resource setups where no retraining can be performed, which shows significant pruning improvements and inference speedups over a state-of-the-art method. LinDeps therefore constitutes an essential add-on for any current or future pruning technique.

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)
• Class-Aware Pruning for Efficient Neural Networks [5.918784236241883]
  Pruning has been introduced to reduce the computational cost in executing deep neural networks (DNNs) In this paper, we propose a class-aware pruning technique to compress DNNs. Experimental results confirm that this class-aware pruning technique can significantly reduce the number of weights and FLOPs.
  arXiv  Detail & Related papers  (2023-12-10T13:07:54Z)
• 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)
• Trainability Preserving Neural Structured Pruning [64.65659982877891]
  We present trainability preserving pruning (TPP), a regularization-based structured pruning method that can effectively maintain trainability during sparsification. TPP can compete with the ground-truth dynamical isometry recovery method on linear networks. It delivers encouraging performance in comparison to many top-performing filter pruning methods.
  arXiv  Detail & Related papers  (2022-07-25T21:15:47Z)
• Boosting Pruned Networks with Linear Over-parameterization [8.796518772724955]
  Structured pruning compresses neural networks by reducing channels (filters) for fast inference and low footprint at run-time. To restore accuracy after pruning, fine-tuning is usually applied to pruned networks. We propose a novel method that first linearly over- parameterizes the compact layers in pruned networks to enlarge the number of fine-tuning parameters.
  arXiv  Detail & Related papers  (2022-04-25T05:30:26Z)
• Interspace Pruning: Using Adaptive Filter Representations to Improve Training of Sparse CNNs [69.3939291118954]
  Unstructured pruning is well suited to reduce the memory footprint of convolutional neural networks (CNNs) Standard unstructured pruning (SP) reduces the memory footprint of CNNs by setting filter elements to zero. We introduce interspace pruning (IP), a general tool to improve existing pruning methods.
  arXiv  Detail & Related papers  (2022-03-15T11:50:45Z)
• Sparse Training via Boosting Pruning Plasticity with Neuroregeneration [79.78184026678659]
  We study the effect of pruning throughout training from the perspective of pruning plasticity. We design a novel gradual magnitude pruning (GMP) method, named gradual pruning with zero-cost neuroregeneration (GraNet) and its dynamic sparse training (DST) variant (GraNet-ST) Perhaps most impressively, the latter for the first time boosts the sparse-to-sparse training performance over various dense-to-sparse methods by a large margin with ResNet-50 on ImageNet.
  arXiv  Detail & Related papers  (2021-06-19T02:09:25Z)
• 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)

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.