  With the substantial performance of neural networks in sensitive fields
increases the need for interpretable deep learning models. Major challenge is
to uncover the multiscale and distributed representation hidden inside the
basket mappings of the deep neural networks. Researchers have been trying to
comprehend it through visual analysis of features, mathematical structures, or
other data-driven approaches. Here, we work on implementation invariances of
CNN-based representations and present an analytical binary prototype that
provides useful insights for large scale real-life applications. We begin by
unfolding conventional CNN and then repack it with a more transparent
representation. Inspired by the attainment of neural networks, we choose to
present our findings as a three-layer model. First is a representation layer
that encompasses both the class information (group invariant) and symmetric
transformations (group equivariant) of input images. Through these
transformations, we decrease intra-class distance and increase the inter-class
distance. It is then passed through a dimension reduction layer followed by a
classifier. The proposed representation is compared with the equivariance of
AlexNet (CNN) internal representation for better dissemination of simulation
results. We foresee following immediate advantages of this toy version: i)
contributes pre-processing of data to increase the feature or class
separability in large scale problems, ii) helps designing neural architecture
to improve the classification performance in multi-class problems, and iii)
helps building interpretable CNN through scalable functional blocks.
