Global Learnable Pooling With Enhancing Distinctive Feature for Image Classification
Xingpeng Zhang, Xiaohong Zhang
Abstract
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 global average pooling. In this paper, we propose a global learnable pooling operation to enhance the distinctive high-level features in the global region, which is codenamed as GLPool. Because it is located before the classification layer, our GLPool is more sensitive to network performance. Besides, GLPool is not a hand-crafted pooling operation, which has the characteristic of adapting to any size of the input. With few parameters is added, GLPool is also a plug-and-play layer. The visualization via class activation map (CAM) on GoogLeNet and ShuffleNet-v2 also shows that GLPool can learn more concentrated and high-level distinctive features than global average pooling. The experiments on several classical deep models demonstrate the significant performance improvements on ImageNet32 and CIFAR100 datasets, which is exceeding obvious for lightweight networks.