Multi Class Out of Distribution

Multi-class out-of-distribution (OOD) detection focuses on identifying data points that differ significantly from the training data, especially when dealing with multiple classes. Current research emphasizes improving the accuracy of OOD detection in multi-class scenarios by refining model architectures (e.g., incorporating feature purification modules and guided decoders) and developing novel algorithms that leverage uncertainty scores from multiple detectors or incorporate semantic information. This is crucial for enhancing the robustness and safety of machine learning models in real-world applications, where encountering unseen data is inevitable, and misclassifications can have serious consequences. Improved OOD detection methods are vital for building more reliable and trustworthy AI systems across various domains.