Boosting Out-of-Distribution Detection Performance from the Perspective of Spatial Feature Importance Research Background and Problem Statement In practical applications of deep learning models, ensuring that models can reliably reject predictions when faced with inputs from unknown categories is crucial for system safety and robustness. This need ...

GL-MCM: Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution Detection Research Background and Problem Statement In real-world applications, machine learning models often face changes in data distribution, such as the emergence of new categories. This phenomenon is known as “Out-of-Distribution Detection (OOD).” To ensure the...