  Deep ensembles are capable of achieving state-of-the-art results in
classification and out-of-distribution (OOD) detection. However, their
effectiveness is limited due to the homogeneity of learned patterns within
ensembles. To overcome this issue, our study introduces Saliency Diversified
Deep Ensemble (SDDE), a novel approach that promotes diversity among ensemble
members by leveraging saliency maps. Through incorporating saliency map
diversification, our method outperforms conventional ensemble techniques and
improves calibration in multiple classification and OOD detection tasks. In
particular, the proposed method achieves state-of-the-art OOD detection
quality, calibration, and accuracy on multiple benchmarks, including
CIFAR10/100 and large-scale ImageNet datasets.
