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[2602.19770] The Confusion is Real: GRAPHIC - A Network Science Approach to Confusion Matrices in Deep Learning
Summary
The paper presents GRAPHIC, a novel approach using network science to analyze confusion matrices in deep learning, enhancing understanding of class relationships during training.
Why It Matters
As explainable AI becomes crucial for developing reliable AI systems, GRAPHIC offers a systematic method to visualize class confusions in neural networks. This insight can help researchers and practitioners identify dataset issues and improve model architectures, ultimately leading to better-performing AI systems.
Key Takeaways
- GRAPHIC provides a network science framework for interpreting confusion matrices.
- The method reveals insights into class separability and dataset ambiguities.
- It allows visualization of learning dynamics across training epochs.
- The approach is architecture-agnostic, applicable to various neural networks.
- Code for implementing GRAPHIC is publicly available, promoting further research.
Computer Science > Machine Learning arXiv:2602.19770 (cs) [Submitted on 23 Feb 2026] Title:The Confusion is Real: GRAPHIC - A Network Science Approach to Confusion Matrices in Deep Learning Authors:Johanna S. Fröhlich, Bastian Heinlein, Jan U. Claar, Hans Rosenberger, Vasileios Belagiannis, Ralf R. Müller View a PDF of the paper titled The Confusion is Real: GRAPHIC - A Network Science Approach to Confusion Matrices in Deep Learning, by Johanna S. Fr\"ohlich and 5 other authors View PDF Abstract:Explainable artificial intelligence has emerged as a promising field of research to address reliability concerns in artificial intelligence. Despite significant progress in explainable artificial intelligence, few methods provide a systematic way to visualize and understand how classes are confused and how their relationships evolve as training progresses. In this work, we present GRAPHIC, an architecture-agnostic approach that analyzes neural networks on a class level. It leverages confusion matrices derived from intermediate layers using linear classifiers. We interpret these as adjacency matrices of directed graphs, allowing tools from network science to visualize and quantify learning dynamics across training epochs and intermediate layers. GRAPHIC provides insights into linear class separability, dataset issues, and architectural behavior, revealing, for example, similarities between flatfish and man and labeling ambiguities validated in a human study. In summary, by uncoverin...
