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- W2912113979 abstract "Abstract Deep residual networks have emerged as a leading technique showing the accuracy and excellent convergence performance. However, because of overfitting and the vanishing gradient problems, we cannot achieve better results by increasing the model parameters. In this study, we propose a novel architecture that has a multi-branch residual network. The shortcut branch of residual networks can ease off the vanishing gradient. The random function and the structure of a multi-branch network improve the fitting ability. The dropout function can weaken overfitting. The proposed method also adopts the adaptive cosine learning rate method and variate batch size to improve the test accuracy. Some experimental investigations are set up to explore the impact of the following factors on the performance: the sequence of the layers, random numbers and different batch sizes, etc. We report that the results achieve 2.63% and 14.2% Top-1 error on CIFAR-10 and CIFAR-100." @default.
- W2912113979 created "2019-02-21" @default.
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- W2912113979 date "2019-02-01" @default.
- W2912113979 modified "2023-10-17" @default.
- W2912113979 title "An improved multi-branch residual network based on random multiplier and adaptive cosine learning rate method" @default.
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- W2912113979 doi "https://doi.org/10.1016/j.jvcir.2019.01.030" @default.
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