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.

翻译：深度集成在分类和分布外（OOD）检测中取得了最优结果；然而，由于集成内部学习模式的同质性，其有效性仍受到限制。为应对这一挑战，本研究提出了一种新方法，通过利用显著性图促进集成成员之间的多样性。通过引入显著性图多样化，该方法在多个分类和OOD检测任务中优于传统集成技术，同时改善了校准性能。在成熟的OpenOOD基准上的实验突显了该方法在实际应用中的潜力。