Constructive Interpretability with CoLabel: Corroborative Integration, Complementary Features, and Collaborative Learning. (arXiv:2205.10011v1 [cs.CV])

Machine learning models with explainable predictions are increasingly sought
after, especially for real-world, mission-critical applications that require
bias detection and risk mitigation. Inherent interpretability, where a model is
designed from the ground-up for interpretability, provides intuitive insights
and transparent explanations on model prediction and performance. In this
paper, we present CoLabel, an approach to build interpretable models with
explanations rooted in the ground truth. We demonstrate CoLabel in a vehicle
feature extraction application in the context of vehicle make-model recognition …

arxiv collaborative cv features integration interpretability learning