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.

翻译：我们提出了一种新型模型、训练流程与架构，用于生成两类图像之间精准的区分图。其目标是以像素级分辨率理解某个类别的独有特征。这些图可促进自监督分割与目标检测，并拓展可解释人工智能（XAI）的新能力。我们提出的架构基于图像分解，其输出为多个生成网络（分支生成对抗网络）的加性总和。类别间的区分被隔离在专用分支中。该方法能够清晰、精准且可解释地可视化每个类别的独有特征。我们展示了该通用方法在多种场景下的应用，例如核磁共振脑肿瘤提取、航拍图像中的车辆分离以及男女性面部特征提取。本文为初步研究结果报告。