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• W2912488996 abstract "One of the ways to train deep neural networks effectively is to use residual connections. Residual connections can be classified as being either identity connections or bridge-connections with a reshaping convolution. Empirical observations on CIFAR-10 and CIFAR-100 datasets using a baseline Resnet model, with bridge-connections removed, have shown a significant reduction in accuracy. This reduction is due to lack of contribution, in the form of feature maps, by the bridge-connections. Hence bridge-connections are vital for Resnet. However, all feature maps in the bridge-connections are considered to be equally important. In this work, an upgraded architecture is proposed to further strengthen the contribution from the bridge-connections by quantifying the importance of each feature map and weighting them accordingly using Squeeze-and-Excitation (SE) block. It is demonstrated that Res-SE-Net generalizes much better than Resnet and SE-Resnet on the benchmark CIFAR-10 and CIFAR-100 datasets." @default.
• W2912488996 created "2019-02-21" @default.
• W2912488996 creator A5001505078 @default.
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• W2912488996 date "2019-02-16" @default.
• W2912488996 modified "2023-09-26" @default.
• W2912488996 title "RES-SE-NET: Boosting Performance of Resnets by Enhancing Bridge-connections" @default.
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• W2912488996 hasPublicationYear "2019" @default.
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