  This paper achieves noteworthy progress in the realm of abstract reasoning,
particularly in addressing Raven's Progressive Matrices (RPM) and Bongard-Logo
challenges. Initially, we introduce Lico-Net, a novel baseline model that
resolves RPM problems with remarkable accuracy. Leveraging this foundation, we
advance with the D3C approach, which advocates representing the underlying
concepts in abstract reasoning problems through distributions. This perspective
enhances the performance of both Lico-Net and a baseline model excelling in
Bongard-Logo tasks. To bolster the computational efficiency of D3C, we present
the D3C-cos variant, offering a streamlined solution. Furthermore, we propose
the D2C method, redefining concept boundaries within these domains and bridging
the divide between high-level abstractions and their lower-dimensional
counterparts. Finally, we extend our methodology to D4C, employing adversarial
techniques to refine concept boundaries further and demonstrate substantial
improvements in both RPM and Bongard-Logo challenges. Overall, our
contributions present a fresh outlook and practical advancements in the field
of abstract reasoning.
