Distribution Detection Task

Out-of-distribution (OOD) detection focuses on enabling machine learning models to reliably identify and reject inputs that differ significantly from their training data, preventing unreliable predictions. Current research emphasizes developing methods that leverage both appearance and motion features (especially in video analysis), utilize generative models to synthesize OOD examples for training, and incorporate human feedback to improve model robustness and generalization. This field is crucial for deploying machine learning models safely and reliably in real-world applications where encountering unseen data is inevitable, impacting areas such as robotics, image classification, and anomaly detection.