Outlier Exposure

Outlier exposure (OE) in machine learning focuses on improving the ability of models to reliably identify and reject data points that differ significantly from the training distribution (out-of-distribution or OOD samples). Current research emphasizes developing methods that effectively leverage auxiliary outlier datasets, often employing techniques like contrastive learning, metric learning, and generative models to enhance OOD detection performance, even in few-shot scenarios. This is crucial for building more robust and trustworthy AI systems across various applications, from image recognition and natural language processing to anomaly detection in complex systems like robotic vision and industrial sound monitoring. The effectiveness of OE is being actively investigated across different model architectures and data modalities, with a strong focus on improving efficiency and reducing reliance on massive outlier datasets.