Gaussian Transfer Convolutional Neural Networks

Hongren Wang, Ce Li, Xiantong Zhen, Wankou Yang, Baochang Zhang

Abstract: In deep convolutional neural networks (DCNN), the pooling operation is usually adopted to produce condensed and transformation invariant feature maps for the input image. However, it will inevitably induce information loss, which has not be addressed yet in designing filters of DCNNs. In this paper, we propose Gaussian transfer convolutional neural networks (GT-CNN), which introduce Gaussian filters to pool convolutional filters of DCNNs. In our GT-CNN, the pooling on features can be transferred to the pooling on filters, which are achieved in the same end-to-end framework. More importantly, the Gaussian filters of multiple scales and orientations further improve the capability of GT-CNN, leading to a more robust feature representation for the input image. We evaluate our GT-CNN on various datasets, including MNIST, CIFAR-10, and CIFAR-100, and achieve the best performance compared with the state of the arts. Moreover, we also apply GT-CNN to the remote sensing image dataset, NWPU-RESISC45 dataset, and validate the superiority of GT-CNN on the task.