OOD Detector

Out-of-distribution (OOD) detection aims to identify data points that differ significantly from the distribution of training data, a crucial task for robust and safe deployment of machine learning models. Current research focuses on improving the accuracy and calibration of OOD detectors, exploring methods based on generative models, pre-trained representations, and analysis of internal network activations, often seeking to surpass the performance of simple likelihood-based approaches. This field is vital for ensuring the reliability of AI systems in real-world applications, particularly in safety-critical domains, and ongoing work emphasizes both improved performance and the development of explainable methods to enhance trust and understanding.