Class-Aware Pruning for Efficient Neural Networks

• URL: http://arxiv.org/abs/2312.05875v2
• Date: Sun, 18 Feb 2024 16:53:29 GMT
• Title: Class-Aware Pruning for Efficient Neural Networks
• Authors: Mengnan Jiang, Jingcun Wang, Amro Eldebiky, Xunzhao Yin, Cheng Zhuo, Ing-Chao Lin, Grace Li Zhang
• Abstract summary: 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.
• Score: 5.918784236241883
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep neural networks (DNNs) have demonstrated remarkable success in various fields. However, the large number of floating-point operations (FLOPs) in DNNs poses challenges for their deployment in resource-constrained applications, e.g., edge devices. To address the problem, pruning has been introduced to reduce the computational cost in executing DNNs. Previous pruning strategies are based on weight values, gradient values and activation outputs. Different from previous pruning solutions, in this paper, we propose a class-aware pruning technique to compress DNNs, which provides a novel perspective to reduce the computational cost of DNNs. In each iteration, the neural network training is modified to facilitate the class-aware pruning. Afterwards, the importance of filters with respect to the number of classes is evaluated. The filters that are only important for a few number of classes are removed. The neural network is then retrained to compensate for the incurred accuracy loss. The pruning iterations end until no filter can be removed anymore, indicating that the remaining filters are very important for many classes. This pruning technique outperforms previous pruning solutions in terms of accuracy, pruning ratio and the reduction of FLOPs. Experimental results confirm that this class-aware pruning technique can significantly reduce the number of weights and FLOPs, while maintaining a high inference accuracy.

Related papers

• Early-Exit with Class Exclusion for Efficient Inference of Neural Networks [4.180653524441411]
  We propose a class-based early-exit for dynamic inference in deep neural networks (DNNs) We take advantage of the learned features in these layers to exclude as many irrelevant classes as possible. Experimental results demonstrate the computational cost of DNNs in inference can be reduced significantly.
  arXiv  Detail & Related papers  (2023-09-23T18:12:27Z)
• 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)
• 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)
• Recursive Least Squares for Training and Pruning Convolutional Neural Networks [27.089496826735672]
  Convolutional neural networks (CNNs) have succeeded in many practical applications. High computation and storage requirements make them difficult to deploy on resource-constrained devices. We propose a novel algorithm for training and pruning CNNs.
  arXiv  Detail & Related papers  (2022-01-13T07:14:08Z)
• Neural Network Pruning Through Constrained Reinforcement Learning [3.2880869992413246]
  We propose a general methodology for pruning neural networks. Our proposed methodology can prune neural networks to respect pre-defined computational budgets. We prove the effectiveness of our approach via comparison with state-of-the-art methods on standard image classification datasets.
  arXiv  Detail & Related papers  (2021-10-16T11:57:38Z)
• Understanding and Improving Early Stopping for Learning with Noisy Labels [63.0730063791198]
  The memorization effect of deep neural network (DNN) plays a pivotal role in many state-of-the-art label-noise learning methods. Current methods generally decide the early stopping point by considering a DNN as a whole. We propose to separate a DNN into different parts and progressively train them to address this problem.
  arXiv  Detail & Related papers  (2021-06-30T07:18:00Z)
• 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)
• Holistic Filter Pruning for Efficient Deep Neural Networks [25.328005340524825]
  "Holistic Filter Pruning" (HFP) is a novel approach for common DNN training that is easy to implement and enables to specify accurate pruning rates. In various experiments, we give insights into the training and achieve state-of-the-art performance on CIFAR-10 and ImageNet.
  arXiv  Detail & Related papers  (2020-09-17T09:23:36Z)
• Robust Pruning at Initialization [61.30574156442608]
  A growing need for smaller, energy-efficient, neural networks to be able to use machine learning applications on devices with limited computational resources. For Deep NNs, such procedures remain unsatisfactory as the resulting pruned networks can be difficult to train and, for instance, they do not prevent one layer from being fully pruned.
  arXiv  Detail & Related papers  (2020-02-19T17:09:50Z)
• Filter Grafting for Deep Neural Networks [71.39169475500324]
  Filter grafting aims to improve the representation capability of Deep Neural Networks (DNNs) We develop an entropy-based criterion to measure the information of filters and an adaptive weighting strategy for balancing the grafted information among networks. For example, the grafted MobileNetV2 outperforms the non-grafted MobileNetV2 by about 7 percent on CIFAR-100 dataset.
  arXiv  Detail & Related papers  (2020-01-15T03:18:57Z)
• Approximation and Non-parametric Estimation of ResNet-type Convolutional Neural Networks [52.972605601174955]
  We show a ResNet-type CNN can attain the minimax optimal error rates in important function classes. We derive approximation and estimation error rates of the aformentioned type of CNNs for the Barron and H"older classes.
  arXiv  Detail & Related papers  (2019-03-24T19:42:39Z)

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.