3 accepted papers
Out-of-distribution (OOD) detection and uncertainty estimation (UE) are critical components for building safe machine learning systems. In this work, we propose a novel framework that combines network inversion with classifier training to simultaneously address both OOD detection and uncertainty est
Shortcut learning, where machine learning models exploit spurious correlations in data instead of capturing meaningful features, poses a significant challenge to building generalizable models. Vision classifiers based on Convolutional Neural Networks (CNNs), Multi-Layer Perceptrons (MLPs), and Visio
Neural networks have emerged as powerful tools across various applications, yet their decision-making process often remains opaque, leading to them being perceived as "black boxes." This opacity raises concerns about their interpretability and reliability, especially in safety-critical scenarios. N…