SQUEEZE-AND-EXCITATION WIDE RESIDUAL NETWORKS IN IMAGE CLASSIFICATION

The depth and width of the network have been investigated to influence the performance of image classification during the resent research. Wide residual networks (WRNs) have proved that the performance of classification can be improved by the width of the networks. With consideration of the significance, expanding the width is to increase the number of channels. However, not all the channels are needed. Meanwhile, much channel information will be lost while exploiting the global average pooling at the end of WRNs for image representations because the mean value is only related to the first order information. With the two considerations stated above, we propose squeeze-and-excitation WRNs which are based on the global covariance pooling (SE-WRNs-GVP). A residual Squeeze-and-Excitation block (rSE-block) can make up for the lost information due to global average pooling in SE-block. Then, informative channels of WRNs will be utilized. Finally, the global covariance pooling at the end of WRNs characterizes the correlations of feature channels for more discriminative representations. A SE-block with dropout is proposed to avoid over-fitting. We conduct experiments on CIFAR10 and CIFAR100 datasets and achieve a better performance without increasing the model complexity.