QPM: Discrete Optimization for Globally Interpretable Image Classification
Thomas Norrenbrock Timo Kaiser Sovan Biswas Ramesh Manuvinakurike Bodo Rosenhahn
Understanding the classifications of deep neural networks, e.g. used in safety-critical situations, is becoming increasingly important. While recent models can locally explain a single decision, to provide a faithful global explanation about an accurate model’s general behavior is a more challenging open task. Towards that goal, we introduce the Quadratic Programming Enhanced Model (QPM), which learns globally interpretable class representations. QPM represents every class with a binary assignment of very few, typically 5, features, that are also assigned to other classes, ensuring easily comparable contrastive class representations. This compact binary assignment is found using discrete optimization based on predefined similarity measures and interpretability constraints. The resulting optimal assignment is used to fine-tune the diverse features, so that each of them becomes the shared general concept between the assigned classes. Extensive evaluations show that QPM delivers unprecedented global interpretability across small and large-scale datasets while setting the state of the art for the accuracy of interpretable models.
explainable AI, Global Interpretability, Sparse Representations, Compactness, Local Interpretability, Image Classification, Discrete Optimization
Using Discrete Optimization to get a Model with faithfully globally interpretable class representations with SOTA Accuracy among interpretable models.