Convolutional neural networks (CNNs) have been widely used and have shown excellent predictive power in image classification. In this paper, an ensemble method, namely the Mixture of trained CNNs (M-tCNNs) method, was proposed to improve the classification performance evaluated on the CIFAR-100 dataset. The M-tCNNs consists of two components, including three expert networks (CNNs) and a trainable gating network. The classification performance of M-tCNNs method was compared to that of single CNN and simple average (SA) method. The results showed that the M-tCNNs method achieved a better accuracy (ACC) of 84.18% compared to single CNN (highest ACC = 78.74%) or SA method (ACC = 80.78%). Experimental results indicate that M-tCNNs method can improve the classification performance for CIFAR-100 dataset compared to single CNN and SA methods.


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    Titel :

    Research on Image Classification Improvement Based on Convolutional Neural Networks with Mixed Training


    Beteiligte:
    Zhang, Yongyue (Autor:in) / Zhang, Junhao (Autor:in) / Zhou, Wenhao (Autor:in)


    Erscheinungsdatum :

    12.10.2022


    Format / Umfang :

    1225560 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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