Abstract
In terms of image classification, in order to obtain higher classification accuracy, different levels of feature information need to be extracted from the image. Convolutional neural networks are increasingly applied to image classification. However, the traditional convolutional neural network has insufficient feature information extraction, poor classification accuracy, and easy over-fitting. This paper proposes Multi-branch aggregation network framework based on deep convolutional neural network that can be applied to image classification. Based on the traditional convolutional nerve, the network width and depth network are increased without increasing the parameters to optimize and improve the network to further enhance the feature expression ability of the network, Enriched the diversity of feature sampling, increased image classification accuracy and prevented overfitting. The framework and traditional frameworks and other frameworks were compared and analyzed through a series of comparative experiments in two standard databases, CIFAR-10 and CIFAR-100, and the validity of the framework was demonstrated.
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Acknowledgments
Authors acknowledge support of the National Natural Science Foundation of China (Grant Nos. 11465004). Authors are also thankful to the anonymous reviewers whose constructive suggestions helped improve and clarify this manuscript.
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Fan, R., Jiang, P., Zeng, S., Li, P. (2019). Multi-branch Aggregate Convolutional Neural Network for Image Classification. In: Liu, X., et al. Service-Oriented Computing – ICSOC 2018 Workshops. ICSOC 2018. Lecture Notes in Computer Science(), vol 11434. Springer, Cham. https://doi.org/10.1007/978-3-030-17642-6_9
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DOI: https://doi.org/10.1007/978-3-030-17642-6_9
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