  Deeper convolutional neural networks provide more capacity to approximate
complex mapping functions. However, increasing network depth imposes
difficulties on training and increases model complexity. This paper presents a
new nonlinear computational layer of considerably high capacity to the deep
convolutional neural network architectures. This layer performs a set of
comprehensive convolution operations that mimics the overall function of the
human visual system (HVS) via focusing on learning structural information in
its input. The core of its computations is evaluating the components of the
structural similarity metric (SSIM) in a setting that allows the kernels to
learn to match structural information. The proposed SSIMLayer is inherently
nonlinear and hence, it does not require subsequent nonlinear transformations.
Experiments conducted on CIFAR-10 benchmark demonstrates that the SSIMLayer
provides better convergence than the traditional convolutional layer, bypasses
the need for nonlinear transformations and shows more robustness against noise
perturbations and adversarial attacks.
