2 research outputs found
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and Accuracy
Deep ensembles achieved state-of-the-art results in classification and
out-of-distribution (OOD) detection; however, their effectiveness remains
limited due to the homogeneity of learned patterns within the ensemble. To
overcome this challenge, our study introduces a novel approach that promotes
diversity among ensemble members by leveraging saliency maps. By incorporating
saliency map diversification, our method outperforms conventional ensemble
techniques in multiple classification and OOD detection tasks, while also
improving calibration. Experiments on well-established OpenOOD benchmarks
highlight the potential of our method in practical applications