Deep Out of Distribution

Deep out-of-distribution (OOD) detection focuses on improving the robustness of machine learning models by identifying and rejecting inputs that differ significantly from the training data. Current research emphasizes developing methods that effectively combine existing post-hoc detection techniques, exploring both appearance and motion features in multimedia data, and leveraging meta-learning and density estimation in latent space to improve generalization across diverse tasks. These advancements are crucial for enhancing the reliability and safety of AI systems in real-world applications, particularly in safety-critical domains where unexpected inputs can have significant consequences.