{"title":"Layer-by-Layer Knowledge Distillation for Training Simplified Bipolar Morphological Neural Networks","authors":"M. V. Zingerenko, E. E. Limonova","doi":"10.1134/s0361768823100080","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Various neuron approximations can be used to reduce the computational complexity of neural networks. One such approximation based on summation and maximum operations is a bipolar morphological neuron. This paper presents an improved structure of the bipolar morphological neuron that enhances its computational efficiency and a new approach to training based on continuous approximations of the maximum and knowledge distillation. Experiments were carried out on the MNIST dataset using a LeNet-like neural network architecture and on the CIFAR10 dataset using a ResNet-22 model architecture. The proposed training method achieves 99.45% classification accuracy on the LeNet-like model (the same accuracy as that provided by the classical network) and 86.69% accuracy on the ResNet-22 model compared with 86.43% accuracy of the classical model. The results show that the proposed method with log-sum-exp (LSE) approximation of the maximum and layer-by-layer knowledge distillation makes it possible to obtain a simplified bipolar morphological network that is not inferior to the classical networks.</p>","PeriodicalId":54555,"journal":{"name":"Programming and Computer Software","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Programming and Computer Software","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1134/s0361768823100080","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Various neuron approximations can be used to reduce the computational complexity of neural networks. One such approximation based on summation and maximum operations is a bipolar morphological neuron. This paper presents an improved structure of the bipolar morphological neuron that enhances its computational efficiency and a new approach to training based on continuous approximations of the maximum and knowledge distillation. Experiments were carried out on the MNIST dataset using a LeNet-like neural network architecture and on the CIFAR10 dataset using a ResNet-22 model architecture. The proposed training method achieves 99.45% classification accuracy on the LeNet-like model (the same accuracy as that provided by the classical network) and 86.69% accuracy on the ResNet-22 model compared with 86.43% accuracy of the classical model. The results show that the proposed method with log-sum-exp (LSE) approximation of the maximum and layer-by-layer knowledge distillation makes it possible to obtain a simplified bipolar morphological network that is not inferior to the classical networks.
期刊介绍：
Programming and Computer Software is a peer reviewed journal devoted to problems in all areas of computer science: operating systems, compiler technology, software engineering, artificial intelligence, etc.
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