  Deep neural network ensembles hold the potential of improving generalization
performance for complex learning tasks. This paper presents formal analysis and
empirical evaluation to show that heterogeneous deep ensembles with high
ensemble diversity can effectively leverage model learning heterogeneity to
boost ensemble robustness. We first show that heterogeneous DNN models trained
for solving the same learning problem, e.g., object detection, can
significantly strengthen the mean average precision (mAP) through our weighted
bounding box ensemble consensus method. Second, we further compose ensembles of
heterogeneous models for solving different learning problems, e.g., object
detection and semantic segmentation, by introducing the connected component
labeling (CCL) based alignment. We show that this two-tier heterogeneity driven
ensemble construction method can compose an ensemble team that promotes high
ensemble diversity and low negative correlation among member models of the
ensemble, strengthening ensemble robustness against both negative examples and
adversarial attacks. Third, we provide a formal analysis of the ensemble
robustness in terms of negative correlation. Extensive experiments validate the
enhanced robustness of heterogeneous ensembles in both benign and adversarial
settings. The source codes are available on GitHub at
https://github.com/git-disl/HeteRobust.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning