Hybrid Gabor Convolutional Networks

Despite great effectiveness of very wide and deep convolutional neural networks (DCNNs) in various computer vision tasks, the significant cost in terms of storage requirement of such networks impedes the deployment on computationally limited devices. Therefore, the network of resource and accuracy trade-offs always have been paid much attention in the popular DCNNs. A new deep model, Hybrid Gabor Convolutional Networks (HGCNs), is proposed in this paper. Incorporating binarized feature map and binarized filters into DCNNs in alternating way, HGCNs alleviate the accuracy loss while reduce the memory storage by 32. Also, HGCNs can be easily implemented and compatible with any other popular deep learning architecture by manipulating the basic structure elements of the DCNNs (i.e., the convolution operator) based on the binarization filter. Most importantly, HGCNs achieve a comparable performance while largely reducing the storage memory compared with state-of-the-art networks such as Resnet. The source code will be public soon.