Guided Convolutional Network

Low-level handcrafted features (e.g., edge and saliency) dominate the design of traditional algorithms, and endow themselves the effective capability of dealing with simple classification problems. However, such excellent properties have not been well explored in the popular deep convolutional neural networks (DCNNs). In this paper, we propose a new deep model, termed Guided Convolutional Networks (GCNs), using low-level handcrafted features to guide the training process of the DCNNs, which can be used in the following vision tasks. Furthermore, signature structure is also investigated with saliency information as a basic block to help the network to be slim. With the modulated binary convolutional way, the memory of our small network is reduced by 132 theoretically. Experiments also demonstrate GCNs have comparable results in presicion compared with state-of-the-art networks such as Wide-ResNet (WRN) while reducing the network dramatically.