  Convolution neural networks (CNNs) are brain-inspired architectures popular
for their ability to train and relearn visually complex tasks. It is
incremental and scalable; however, CNN is mostly treated as black-box and
involves multiple trial & error runs. We observe that CNN constructs powerful
internal representations that help achieve state-of-the-art performance. Here
we propose three layer glass-box (analytical) CNN for two-class image
classifcation problems. First is a representation layer that encompasses both
the class information (group invariant) and symmetric transformations (group
equivariant) of input images. It is then passed through dimension reduction
layer (PCA). Finally the compact yet complete representation is provided to a
classifer. Analytical machine learning classifers and multilayer perceptrons
are used to assess sensitivity. Proposed glass-box CNN is compared with
equivariance of AlexNet (CNN) internal representation for better understanding
and dissemination of results. In future, we would like to construct glass-box
CNN for multiclass visually complex tasks.
