Post Hoc Out of Distribution

Post-hoc out-of-distribution (OOD) detection aims to identify data points that differ significantly from a model's training distribution, a crucial task for deploying reliable AI systems. Current research focuses on improving the accuracy and efficiency of these detectors, exploring techniques like leveraging diffusion models to generate more realistic synthetic OOD data, analyzing the robustness of existing methods to adversarial attacks and noisy labels, and developing computationally efficient algorithms based on decision boundaries or neuron activation patterns. These advancements are vital for enhancing the safety and reliability of machine learning models across various applications, particularly in high-stakes domains where misclassifications can have serious consequences.