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Pooling layers appear widely in deep networks for its aggregating information in a local region and fast downsampling. Due to the reason that the closer to the output layer, the more the network learns is the high-level semantic information related to classification, the global average pooling would inhibit the contribution of local high magnitudes features in the global region. Besides, the gradient of the distinctive features is considerably attenuated due to the large region size of globaldoi:10.1109/access.2020.2997078 fatcat:opn3xdazmvcvxk7ijeo4gt43jq