  Steerable properties dominate the design of traditional filters, e.g., Gabor
filters, and endow features the capability of dealing with spatial
transformations. 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 Gabor Convolutional Networks (GCNs or Gabor
CNNs), which incorporates Gabor filters into DCNNs to enhance the resistance of
deep learned features to the orientation and scale changes. By only
manipulating the basic element of DCNNs based on Gabor filters, i.e., the
convolution operator, GCNs can be easily implemented and are compatible with
any popular deep learning architecture. Experimental results demonstrate the
super capability of our algorithm in recognizing objects, where the scale and
rotation changes occur frequently. The proposed GCNs have much fewer learnable
network parameters, and thus is easier to train with an end-to-end pipeline.
