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Deep ensembles are capable of achieving state-of-the-art results inclassification and out-of-distribution (OOD) detection. However, theireffectiveness is limited due to the homogeneity of learned patterns withinensembles. To overcome this issue, our study introduces Saliency DiversifiedDeep Ensemble (SDDE), a novel approach that promotes diversity among ensemblemembers by leveraging saliency maps. Through incorporating saliency mapdiversification, our method outperforms conventional ensemble techniques andimproves calibration in multiple classification and OOD detection tasks. Inparticular, the proposed method achieves state-of-the-art OOD detectionquality, calibration, and accuracy on multiple benchmarks, includingCIFAR10/100 and large-scale ImageNet datasets.

Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and Accuracy