Improving out-of-distribution detection in normalizing flows with synthetic outliers

Key Points

• Enhancements in out-of-distribution detection show a significant increase in accuracy with synthetic outliers.
• The method improved detection rates by 35% when synthetic outliers were integrated into the model.
• Analysis using normalizing flows demonstrated effective anomaly detection techniques across diverse datasets.
• Improvements call for further validation in real-world scenarios, ensuring applicability across varied data distributions.