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ConvNet combined with minimum weighted random search algorithm for improving the domain shift problem of image recognition model

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Abstract

The improved algorithm for attribute combinatorial optimization based on the Minimum Weighted Random Search (MWRS) and “Equal-Sum” (E-S) judgment method is proposed to against that the recognition performance of the image recognition model trained by a specific dataset is significantly reduced after being transplanted. First, the MWRS algorithm is used to search the image attribute combination and the searched attribute combination is filtered through the E-S judgment method. Then, the image that being transformed by the selected attribute combination is input to the improved neural network. Finally, the Adam optimization algorithm is used to train the model. In this context, the Minimum Weighted Random Search Data Augmentation (MW-RSDA), the Minimum Weighted Evolution-Based Search Data Augmentation (MW-ESDA) and the Minimum Weighted Random and Evolution-Based Search Data Augmentation (MW-RESDA) were proposed. The experimental results show that the overall recognition accuracy is increased by at least 3.7% after the improved digits recognition model is transplanted. Moreover, the improved CIFAR-10 (a 10-class dataset named after Canadian Institute for Advanced Research) classification model increased the recognition accuracy by at least 5.8% on the testing dataset, which significantly improve the influence of domain shift to recognition model.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 102616, China

    Zhi Tan & Xingye Liu

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  1. Zhi Tan
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  2. Xingye Liu
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Correspondence to Xingye Liu.

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Tan, Z., Liu, X. ConvNet combined with minimum weighted random search algorithm for improving the domain shift problem of image recognition model. Appl Intell 52, 6889–6904 (2022). https://doi.org/10.1007/s10489-021-02767-8

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