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A Mutual Learning Framework for Pruned and Quantized Networks/Un marco de aprendizaje mutuo para redes podadas y cuantificadas

Journal of Computer Science & Technology, 2023-04, Vol.23 (1), p.1 [Peer Reviewed Journal]

COPYRIGHT 2023 Graduate Network of Argentine Universities with Computer Science Schools (RedUNCI) ;ISSN: 1666-6046 ;DOI: 10.24215/16666038.23.e01

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  • Title:
    A Mutual Learning Framework for Pruned and Quantized Networks/Un marco de aprendizaje mutuo para redes podadas y cuantificadas
  • Author: Li, Xiaohai ; Chen, Yiqiang ; Wang, Jindong
  • Subjects: Algorithms ; Analysis
  • Is Part Of: Journal of Computer Science & Technology, 2023-04, Vol.23 (1), p.1
  • Description: Model compression is an important topic in deep learning research. It can be mainly divided into two directions: model pruning and model quantization. However, both methods will more or less affect the original accuracy of the model. In this paper, we propose a mutual learning framework for pruned and quantized networks. We regard the pruned network and the quantized network as two sets of features that are not parallel. The purpose of our mutual learning framework is to better integrate the two sets of features and achieve complementary advantages, which we call feature augmentation. To verify the effectiveness of our framework, we select a pairwise combination of 3 state-of-the-art pruning algorithms and 3 state-of-the-art quantization algorithms. Extensive experiments on CIFAR-10, CIFAR-100 and Tiny-imagenet show the benefits of our framework: through the mutual learning of the two networks, we obtain a pruned network and a quantization network with higher accuracy than traditional approaches.
  • Publisher: Graduate Network of Argentine Universities with Computer Science Schools (RedUNCI)
  • Language: Spanish
  • Identifier: ISSN: 1666-6046
    DOI: 10.24215/16666038.23.e01
  • Source: ROAD: Directory of Open Access Scholarly Resources
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