OOD Detection

Out-of-distribution (OOD) detection focuses on identifying data points that deviate significantly from the distribution of training data used to build a machine learning model. Current research emphasizes developing robust methods for various data types (images, graphs, molecular structures, tabular data) and model architectures (including vision transformers, generative models like diffusion models, and graph neural networks), often incorporating techniques like uncertainty quantification and representation learning to improve detection accuracy. Effective OOD detection is crucial for ensuring the reliability and safety of deployed machine learning systems across diverse applications, from medical diagnosis to autonomous driving, by mitigating the risks associated with unexpected or adversarial inputs.