  We present a method for learning discriminative filters using a shallow
Convolutional Neural Network (CNN). We encode rotation invariance directly in
the model by tying the weights of groups of filters to several rotated versions
of the canonical filter in the group. These filters can be used to extract
rotation invariant features well-suited for image classification. We test this
learning procedure on a texture classification benchmark, where the
orientations of the training images differ from those of the test images. We
obtain results comparable to the state-of-the-art. Compared to standard shallow
CNNs, the proposed method obtains higher classification performance while
reducing by an order of magnitude the number of parameters to be learned.
