Scalable Ensemble Diversification for Improved Out-of-Distribution Generalization and Detection

Scalable Ensemble Diversification (SED) is a method that enables training diverse ensembles without requiring separate out-of-distribution (OOD) data, leading to improved OOD generalization and detection.