Structural Pruning via Spatial-aware Information Redundancy for Semantic Segmentation

• URL: http://arxiv.org/abs/2412.12672v1
• Date: Tue, 17 Dec 2024 08:41:50 GMT
• Title: Structural Pruning via Spatial-aware Information Redundancy for Semantic Segmentation
• Authors: Dongyue Wu, Zilin Guo, Li Yu, Nong Sang, Changxin Gao,
• Abstract summary: We argue that most existing pruning methods, originally designed for image classification, overlook the fact that segmentation is a location-sensitive task. This paper proposes a novel approach, denoted as Spatial-aware Information Redundancy Filter Pruning, which aims to reduce feature redundancy between channels.
• Score: 34.554924043562295
• License:
• Abstract: In recent years, semantic segmentation has flourished in various applications. However, the high computational cost remains a significant challenge that hinders its further adoption. The filter pruning method for structured network slimming offers a direct and effective solution for the reduction of segmentation networks. Nevertheless, we argue that most existing pruning methods, originally designed for image classification, overlook the fact that segmentation is a location-sensitive task, which consequently leads to their suboptimal performance when applied to segmentation networks. To address this issue, this paper proposes a novel approach, denoted as Spatial-aware Information Redundancy Filter Pruning~(SIRFP), which aims to reduce feature redundancy between channels. First, we formulate the pruning process as a maximum edge weight clique problem~(MEWCP) in graph theory, thereby minimizing the redundancy among the remaining features after pruning. Within this framework, we introduce a spatial-aware redundancy metric based on feature maps, thus endowing the pruning process with location sensitivity to better adapt to pruning segmentation networks. Additionally, based on the MEWCP, we propose a low computational complexity greedy strategy to solve this NP-hard problem, making it feasible and efficient for structured pruning. To validate the effectiveness of our method, we conducted extensive comparative experiments on various challenging datasets. The results demonstrate the superior performance of SIRFP for semantic segmentation tasks.

Related papers

• Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition [5.656581242851759]
  Magnitude Pruning is a lightweight network design method which seeks to remove connections with the smallest magnitude. We devise a novel coarse-to-fine (CTF) method that gathers the advantages of structured and unstructured pruning. Our method relies on a novel CTF parametrization that models the mask of each connection as the Hadamard product.
  arXiv  Detail & Related papers  (2024-12-17T13:11:48Z)
• SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models [19.479746878680707]
  Layer pruning is a potent approach to reduce network size and improve computational efficiency. We propose a Similarity Guided fast Layer Partition pruning for compressing large deep models. Our method outperforms the state-of-the-art methods in both accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2024-10-14T04:01:08Z)
• A Multi-objective Complex Network Pruning Framework Based on Divide-and-conquer and Global Performance Impairment Ranking [40.59001171151929]
  A multi-objective complex network pruning framework based on divide-and-conquer and global performance impairment ranking is proposed in this paper. The proposed algorithm achieves a comparable performance with the state-of-the-art pruning methods.
  arXiv  Detail & Related papers  (2023-03-28T12:05:15Z)
• 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)
• Cascaded Sparse Feature Propagation Network for Interactive Segmentation [18.584007891618096]
  We propose a cascade sparse feature propagation network that learns a click-augmented feature representation for propagating user-provided information to unlabeled regions. We validate the effectiveness of our method through comprehensive experiments on various benchmarks, and the results demonstrate the superior performance of our approach.
  arXiv  Detail & Related papers  (2022-03-10T03:47:24Z)
• CATRO: Channel Pruning via Class-Aware Trace Ratio Optimization [61.71504948770445]
  We propose a novel channel pruning method via Class-Aware Trace Ratio Optimization (CATRO) to reduce the computational burden and accelerate the model inference. We show that CATRO achieves higher accuracy with similar cost or lower cost with similar accuracy than other state-of-the-art channel pruning algorithms. Because of its class-aware property, CATRO is suitable to prune efficient networks adaptively for various classification subtasks, enhancing handy deployment and usage of deep networks in real-world applications.
  arXiv  Detail & Related papers  (2021-10-21T06:26:31Z)
• 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)
• MTP: Multi-Task Pruning for Efficient Semantic Segmentation Networks [32.84644563020912]
  We present a multi-task channel pruning approach for semantic segmentation networks. The importance of each convolution filter wrt the channel of an arbitrary layer will be simultaneously determined by the classification and segmentation tasks. Experimental results on several benchmarks illustrate the superiority of the proposed algorithm over the state-of-the-art pruning methods.
  arXiv  Detail & Related papers  (2020-07-16T15:03:01Z)
• Discretization-Aware Architecture Search [81.35557425784026]
  This paper presents discretization-aware architecture search (DAtextsuperscript2S) The core idea is to push the super-network towards the configuration of desired topology, so that the accuracy loss brought by discretization is largely alleviated. Experiments on standard image classification benchmarks demonstrate the superiority of our approach.
  arXiv  Detail & Related papers  (2020-07-07T01:18:58Z)
• Boundary-assisted Region Proposal Networks for Nucleus Segmentation [89.69059532088129]
  Machine learning models cannot perform well because of large amount of crowded nuclei. We devise a Boundary-assisted Region Proposal Network (BRP-Net) that achieves robust instance-level nucleus segmentation.
  arXiv  Detail & Related papers  (2020-06-04T08:26:38Z)
• Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks [100.14670789581811]
  We train a graph convolutional network to fit the performance of sampled sub-networks. With this strategy, we achieve a higher rank correlation coefficient in the selected set of candidates.
  arXiv  Detail & Related papers  (2020-04-17T19:12:39Z)

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.