Improving Robustness and Calibration in Ensembles with Diversity Regularization

Calibration and uncertainty estimation are crucial topics in high-risk environments. Following the recent interest in the diversity of ensembles, we systematically evaluate the viability of explicitly regularizing ensemble diversity to improve robustness and calibration on in-distribution data as well as under dataset shift. We introduce a new diversity regularizer for classification tasks that uses out-of-distribution samples and increases the overall accuracy, calibration and out-of-distribution detection capabilities of ensembles. We demonstrate that diversity regularization is highly beneficial in architectures where weights are partially shared between the individual members and even allows to use fewer ensemble members to reach the same level of robustness. Experiments on CIFAR-10, CIFAR-100, and SVHN show that regularizing diversity can have a significant impact on calibration and robustness, as well as out-of-distribution detection.