  We present a new model, training procedure and architecture to create precise
maps of distinction between two classes of images. The objective is to
comprehend, in pixel-wise resolution, the unique characteristics of a class.
These maps can facilitate self-supervised segmentation and objectdetection in
addition to new capabilities in explainable AI (XAI). Our proposed architecture
is based on image decomposition, where the output is the sum of multiple
generative networks (branched-GANs). The distinction between classes is
isolated in a dedicated branch. This approach allows clear, precise and
interpretable visualization of the unique characteristics of each class. We
show how our generic method can be used in several modalities for various
tasks, such as MRI brain tumor extraction, isolating cars in aerial photography
and obtaining feminine and masculine face features. This is a preliminary
report of our initial findings and results.
