  Regularization techniques are widely used to improve the generality,
robustness, and efficiency of deep convolutional neural networks (DCNNs). In
this paper, we propose a novel approach of regulating DCNN convolutional
kernels by a structured filter bank. Comparing with the existing regularization
methods, such as $\ell_1$ or $\ell_2$ minimization of DCNN kernel weights and
the kernel orthogonality, which ignore sample correlations within a kernel, the
use of filter bank in regularization of DCNNs can mold the DCNN kernels to
common spatial structures and features (e.g., edges or textures of various
orientations and frequencies) of natural images. On the other hand, unlike
directly making DCNN kernels fixed filters, the filter bank regularization
still allows the freedom of optimizing DCNN weights via deep learning. This new
DCNN design strategy aims to combine the best of two worlds: the inclusion of
structural image priors of traditional filter banks to improve the robustness
and generality of DCNN solutions and the capability of modern deep learning to
model complex non-linear functions hidden in training data. Experimental
results on object recognition tasks show that the proposed regularization
approach guides DCNNs to faster convergence and better generalization than
existing regularization methods of weight decay and kernel orthogonality.
